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Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone. Validerad;2023;Nivå 1;2023-02-17 (joosat);Licens fulltext: CC BY License

Groundwater management requires a systematic approach since it is crucial to the long-term viability of livelihoods and regional economies all over the world. There is insufficient groundwater management and difficulties in storage plans as a result of increased population, fast urbanisation, and climate change, as well as unpredictability in rainfall frequency and intensity. Groundwater exploration using remote sensing (RS) data and geographic information system (GIS) has become a breakthrough in groundwater research, assisting in the assessment, monitoring, and conservation of groundwater resources. The study region is the Mand catchment of the Mahanadi basin, covering 5332.07 km2 and is located between 21°42′15.525″N and 23°4′19.746″N latitude and 82°50′54.503″E and 83°36′1.295″E longitude in Chhattisgarh, India. The research comprises the generation of thematic maps, delineation of groundwater potential zones and the recommendation of structures for efficiently and successfully recharging groundwater utilising RS and GIS. Groundwater Potential Zones (GPZs) were identified with nine thematic layers using RS, GIS, and the Multi-Criteria Decision Analysis (MCDA) method. Satty's Analytic Hierarchy Process (AHP) was used to rank the nine parameters that were chosen. The generated GPZs map indicated regions with very low, low to medium, medium to high, and very high groundwater potential encompassing 962.44 km2, 2019.92 km2, 969.19 km2, and 1380.42 km2 of the study region, respectively. The GPZs map was found to be very accurate when compared with the groundwater fluctuation map, and it is used to manage groundwater resources in the Mand catchment. The runoff of the study area can be accommodated by the computing subsurface storage capacity, which will raise groundwater levels in the low and low to medium GPZs. According to the study results, various groundwater recharge structures such as farm ponds, check dams and percolation tanks were suggested in appropriate locations of the Mand catchment to boost groundwater conditions and meet the shortage of water resources in agriculture and domestic use. This study demonstrates that the integration of GIS can provide an efficient and effective platform for convergent analysis of various data sets for groundwater management and planning. Validerad;2023;Nivå 2;2023-06-19 (joosat);Licens fulltext: CC BY License

Uncertainty is a common problem in spatial modeling and geographical information systems (GIS). Furthermore, urban gain modeling (UGM) contains various dimensions and components of uncertainties. Data sampling is important in UGM, and may cause the results of the models to contain many uncertainties as well as affects their precision and accuracy. A poorly sampled or biased dataset can lead to inaccurate predictions and decreased performance of the models. This paper aims to present and develop novel strategies for sampling and building training datasets that can enhance the performance of data-driven models. In other words, the present study used maximum entropy (ME) and ecological niche factor analysis (ENFA) models to select pure non-change samples with minimal uncertainty for training datasets in UGM of Isfahan and Tabriz cities in Iran. The urban gain of two time intervals of 1992–2002 and 2002–2012 were used for Tabriz City and two time intervals of 1994–2004 and 2004–2014 for Isfahan City. Nine and 14 urban gain drivers were used in the UGM of Isfahan and Tabriz cities, respectively. After the ME and ENFA models produced a training dataset with change and non-change samples with the lowest uncertainty, three well-known models, namely random forest (RF), artificial neural network (ANN), and support vector machine (SVM) were used for the modeling. Moreover, the ME and ENFA models that were used to investigate the uncertainty of the sampling procedure were used as the one-class prediction models. Compared to extant studies, the proposed ME – based sampling strategy increased the area under the receiver operating characteristic curve (AUROC), figure of merit, producer’s accuracy, and overall accuracy by 5.5%, 5%, 5%, and 3%, respectively, in the validation phase of Isfahan City and by 5%, 6%, 14%, and 17%, respectively, for Tabriz City. For Isfahan, the accuracies of ME (AUROC = 0.649) and ENFA (AUROC = 0.661) one – class models were closer to that of the ANN – ME (AUROC = 0.646), ANN – ENFA (AUROC = 0.619), and RF – ENFA (AUROC = 0.631) models but differed significantly from that of the RF – ME (AUROC = 0.737) model. For Tabriz, the accuracies of ME (AUROC = 0.657) and ENFA (AUROC = 0.688) one – class models were lower than that of the two class RF-ME (AUROC = 0.852), and ANN-ME (AUROC = 0.778) models. The results showed that the ME model was able to identify relatively pure non-change samples and properly remove impure non-change samples from the training dataset. This study discovered that binary models are preferable to one-class models, and showed that an optimal sampling strategy is an essential step in UGM as it can decrease uncertainty. As such, modelers must adopt efficient sampling methods. Validerad;2023;Nivå 2;2023-06-14 (joosat);Licens fulltext: CC BY License

The Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves – Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region. Validerad;2023;Nivå 2;2023-04-20 (hanlid);Funder: Higher Education Commission (HEC) Pakistan (8899, NRPU)

A 3D density structure of the lithosphere and upper mantle beneath the eastern Mediterranean Sea (EMS) and its adjacent region was constructed based on gravity anomaly inversion constrained by a seismic tomography model. Gravity effects of terrain and crust were removed from the observed gravity field (EIGEN-6C4) to obtain the residual mantle gravity anomaly (RMGA). The density distribution of the lithosphere and upper mantle was investigated. The 3D inversion process was constrained by an initial density model projected from the shear-wave velocity model (SL2013sv). The results show some characteristics of the density distribution in the lithosphere and in the upper mantle that could be related to the tectonic importance of the Mediterranean Sea and the surrounding region. A low-density zone dominates the lithosphere beneath the Sea except for the area around the Arabia Shield and North Anatolian Fault belt. A thinner, high-density layer beneath the southwest of the Sea may be related to the older oceanic lithospheric fragments. The high-density anomalies appear at depths below 280 km beneath the Sea and the Turkish Aegean Sea Plate. However, the low-density anomalies appear on the upper mantle under the trenches of the southwestern part of the Mediterranean Sea, the eastern part of the Aegean Sea, the Red Sea, the Black Sea, and the middle of the Arabia shield. The deep structure under the Eratosthenes seamount in the Mediterranean Sea is the source of the intensity and genesis of tectonic activity. Furthermore, the convergence region of two low-density anomaly zones (Africa-Anatolia) may be interpreted as a significant tectonic unit (Eratosthenes seamount) caused by the arrival of the relatively thick and buoyant Eratosthenes block to its present location south of Cyprus in Holocene time based on the density model interpretation beneath the Mediterranean Sea during the Late Cretaceous and early Tertiary period. Validerad;2023;Nivå 2;2023-05-31 (joosat);Funder: Natural Science Foundation of China (Nos. 42174090, 41604060); the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources (MSFGPMR2022-4), China University of Geosciences

The dynamic of nutrient cycling is a critical factor in riparian regions. It is essential to understand the behaviour of riparian areas in the maintenance and management river ecosystem. Sediment load, nutrients, and pathogens are transported to water bodies through land drainage and riverside flow. The classification of environmental agencies was poor for them. In this study, a qualitative investigation was implemented to determine the relationship between these practices and variations in nutrient retention for several types of riverbank soil. Also, the riverbank soils were including soil covered by wild reed plants. All the field works were along the Euphrates River in three locations. Moreover, study the variation in the content of vegetation riverbank soils from nitrogen, organic matter (OM), potassium (K), phosphorus (P), and PH. The results presented that riverbanks consider important locations for nutrient retention. Whilst agricultural activities have minimized the content of soil of OM (30%), N (49%), and K (3%), in subsurface soil but not so great lowering in surface layers. In contrast, management practices and human activities such as burning caused an apparent increase of OM (4%), N (77%), and a clear reduction in P (12%) content at both surface and subsurface layers of soil. Under all circumstances, riverbank soils showed a relative increase of nutrients at wet toe-slopes. Furthermore, it is noted that riparian vegetation and aquatic plants played a significant role by causing critical changes in riparian sides or even contrary effects on riverbank management practices and destruction of natural soil nutrient conditions. Thus, it should be carefully considered when evaluating the ecological impacts of riparian disturbances. Validerad;2023;Nivå 1;2023-03-08 (joosat);Funder: Al-Mustaqbal University College, Iraq

Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968–0.988 and RMSE = 1.274–1.403 MJ/m2⋅d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR. Hence, this study recommends new effective methods to predict GSR in semi-arid regions. Validerad;2022;Nivå 2;2022-09-23 (joosat)

Flash floods are one of the most dangerous natural disasters, especially in hilly terrain, causing loss of life, property, and infrastructures and sudden disruption of traffic. These types of floods are mostly associated with landslides and erosion of roads within a short time. Most of Vietnam is hilly and mountainous; thus, the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management. In this study, three Machine Learning (ML) methods namely Deep Learning Neural Network (DL), Correlation-based Feature Weighted Naive Bayes (CFWNB), and Adaboost (AB-CFWNB) were used for the development of flash flood susceptibility maps for hilly road section (115 km length) of National Highway (NH)-6 in Hoa Binh province, Vietnam. In the proposed models, 88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors. The performance of the models was evaluated using standard statistical measures including Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC) and Root Mean Square Error (RMSE). The results revealed that all the models performed well (AUC > 0.80) in predicting flash flood susceptibility zones, but the performance of the DL model is the best (AUC: 0.972, RMSE: 0.352). Therefore, the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area. Validerad;2022;Nivå 2;2022-11-28 (hanlid);Funder: Vietnam National Foundation for Science and Technology Development (NAFOSTED) (105.08-2019.03)

Sporadic burning of rice straw and the particulate air pollution caused consequently have created a pressing need for identification of practical environmentally sound in situ rice residue management methods. However, the agronomic interventions associated with the agri-inputs particularly the type of nitrogen fertilizer source must be worked out for these interventions. In a two-year field study performed at two different locations representing sandy loam and clay loam soil types, zero tillage with application of nitrophosphate (applied as basal dose through drilling) in combination with urea (applied at 1st irrigation + 3 foliar sprays of urea at weekly interval) significantly enhanced the grain and straw yield of wheat. The soil microbial viable cell counts and dehydrogenase and urease enzyme activities were also recorded to be highest in this treatment indicating the occurrence of higher living microbial population. The treatment × response variable Principle component analysis (PCA) biplot depicted relative variation among the residue management treatments/Nitrogen fertilizer sub-treatments and the enzyme activities as response variables. A variation in the soil organic content components was recognized through Fourier transform infra-red spectroscopy (FT-IRS) studies. Irrespective of the soil types under study, the FT-IR spectra exhibited presence of the aromatic carbon functional groups in residue incorporated treatments as compared to the no residue incorporation treatment. Validerad;2023;Nivå 2;2023-06-07 (hanlid);Funder: Research Chair of Prince Sultan Bin Abdulaziz International Prize for Water, King Saud University

This study assessed the quality of water in the Shatt Al-Hillah River by adopting some variables of physical, chemical, and heavy metal elements. The samples have been taken at six sites along the river in 2020 (from January to December). The water quality index has been determined by using the weighted-arithmetic method which is including a series of equations. Also, the model of Inverse-Distance-Weighting in the Geographic information system was applied to create a map of the water quality in the study area. Eleven physicochemical variables and five elements of heavy metals were comprised of calcium, magnesium, dissolved oxygen, Hydrogen Ions, chloride, sulfate, total hardness, total dissolved solids, turbidity, alkalinity, electric conductivity, cadmium, copper, iron, lead, and zinc. The results showed the values of the water quality index ranged from 245 to 253 (with a category of 200–300). The water quality index was rated as very poor for the selected locations along the Shatt Al-Hillah River. The GIS result illustrated the distributing map of water quality for the Shatt Al-Hillah River for household uses. The combination of the water quality index calculations with GIS in the current study might be used as a guide for future studies. Validerad;2023;Nivå 2;2023-06-12 (hanlid)

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management. Validerad;2023;Nivå 2;2023-06-19 (hanlid);Funder: University of Kurdistan, Iran (02-9-3786, 01-9-22595)

The purpose of this research is to delineate the structures of the Lower Goru Formation, investigate fluid properties, and clarify the hydrocarbon-prone areas through seismic attributes analysis. First, the acquired data was matched by the interpretation datum. Structural analysis was done by performing horizon interpretation, fault interpretation, and contour mapping on the C-Interval of the Lower Goru Formation. Hydrocarbon zones were marked with the help of attribute analysis on seismic sections and were justified by petrophysical analysis. An integrated approach such as seismic structural interpretation, seismic attribute, spectral decomposition, and petrophysical analyses was used in current research to better understand geological structure and features. This research showed that normal faults are present in the area showing negative flower structure, horst and graben, and faults oriented north-west to south-east. The contour map shows structural inclination and faults bound closure near well locations. Variance attribute and spectral decomposition attribute were used to verify horizon lineation and fault behavior. Instantaneous amplitude and instantaneous phase attributes justify hydrocarbon bearing zones, and bright spots are present on seismic sections at C–Interval of Lower Goru Formation. Petrophysical analysis of the available wells showed a number of significant hydrocarbon zones having more than 55% of hydrocarbon saturation at the C-Interval of the Lower Goru Formation. The four possible reservoir zones in Sawan-02 well, two zones in Sawan-07 well, and three zones in Sawan-09 well are identified based on well data interpretation. Based on these analyses, the area of interest has a very good reservoir potential, structural closure, and visible bright spots. The current finding of this research will be helpful for future exploration and development of the Sawan area. Validerad;2023;Nivå 2;2023-02-08 (joosat);Licens fulltext: CC BY License

By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, CODinlet, and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, CODinlet=600mg/l, and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 mg/l, showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient (R2) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively. Validerad;2023;Nivå 2;2023-06-12 (joosat);Licens fulltext: CC BY License

In times of water scarcity, groundwater is a vital resource that provides an alternate source of water for human consumption. In Iraq, the quality of rivers has been greatly affected by climate change and the dwindling availability of surface water. Examining and classifying the groundwater in this region is now vital. The present study sought to incorporate the groundwater property data (drinking purpose) with a geographic information system (GIS). Eleven variables were measured in 25 wells to investigate the physio-chemical properties around the Babylon province of Iraq. Based on the acceptability of groundwater for drinking, the GWQI was categorized into four primary groups in the results. Approximately 28% of the twenty-five wells (1811.04 km2) are of excellent quality, 24% are of good quality (1552.3 km2), 44% are of low quality (2845.9 km2), and 4% are extremely contaminated. (2587.2 km2). The average GWQI for the entire study region was 110.7, making it inappropriate for human consumption. It has been determined that approximately 52% of the groundwater from the examined wells can be deemed safe for consumption, although certain measurements surpass the permissible limits. To guarantee residents in these areas are supplied with water of superior quality and safety, treatment of the tested groundwater is recommended before use. Validerad;2023;Nivå 2;2023-06-29 (joosat);Licens fulltext: CC BY License

Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model's predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Validerad;2023;Nivå 2;2023-04-21 (sofila);Funder: Deanship of Scientific Research, King Khalid University (grant no. (R.G.P. 2/43/44)

Many aquatic ecosystems are vulnerable to many pollutants, particularly heavy metals that are now a threat to public health. It may be more effective to manage pollution in waterways if we can better understand the connection between heavy metals in sediments and their accumulation in green mussels. In this study, lead levels in green mussels (Perna viridis) were above the permissible maximum level (WHO, Malaysia Food Regulation, and the FAO). Moreover, zinc levels exceeded the FAO's maximum recommended range. The fractionate of heavy metals in surface sediment from the eastern part of the Johore Straits was investigated using the sequential extraction techniques (SET) method. In the majority of sampling stations, copper's chemical speciation followed the following order: residual > oxidisable-organic > exchangeable > acid reducible, while zinc's and lead's chemical speciation followed the following pattern: residual > oxidisable-organic > acid reducible > exchangeable. The results of Pearson's correlation studies demonstrated a substantial association between Zn concentration in the tissues of green mussels and total Zinc, exchangeable (F1), acid-reducible (F2), and residual (F4) Zn in surface sediment. Green mussel Cu content and surface sediment Cu of exchangeable (F1), residual (F4), and total Cu were found to correlate. As a result, green mussels are being used as a biomonitoring agent for heavy metal contamination on the eastern side of the Straits of Johor in Malaysia. Godkänd;2023;Nivå 0;2023-07-05 (hanlid);Funder: Malaysian Ministry of Higher Education (KPT.P. (S) 400‐7/2/29‐4 (65))

A well-known tool for assessing the quality of surface water is the water quality index (WQI) model. In this study, the WQI was generated to classify the water flowing in the Euphrates River in Qadisiyah Province. To develop analytical models, a connection between the findings and satellite images was developed. It is possible to determine what category a river’s water quality for domestic use will fall into. The Weighted Arithmetic Water Quality Index (WWQI), Canadian Water Quality Index (CWQI),and Bascarón Water Quality Index (BWQI) were used to evaluate and examine the suitability of the Euphrates River in the city by analysing the water quality of samples taken from the five locations (Muhanawia (L1), Salahia (L2), Shamiyah (L3), Shamiyah (L4), Gammas (L5)). The hydrogen ionspH, temperature T, dissolved oxygen DO, nitrate NO3, calcium Ca, magnesium Mg, total hardness TH, potassium K, sodium Na, sulfate SO4, chlorine Cl, total dissolved solids TDS, and electrical conductivity ECvalues are provided for 2020 and 2021. Results showed the Euphrates River was deemed severely contaminated at location Gammas (L5) but acceptable at location Muhanawia (L1). During the research phase, the water quality for the Euphrates achieved a maximum of 87.43 using the CWQI for Muhanawia (L1) in 2021 and a minimum of 15.6 using the BWQI for Gammas (L5) in 2021. The excessive sulphate, total dissolved solids, calcium, and total hardness concentrations led to the low WQI. The results are analysed using a GIS, and a network database connected to the GIS is required to utilize its analytical capabilities and the geographically scattered data throughout the study region. The Water Quality Index (WQI) is not suitable for drinking, as it is below the average of the World Health Organization (WHO) suggestions. Funder: Al-Mustaqbal University College, Iraq

This study investigates the effectiveness of using Iraqi clay as a low-permeability layer to prevent the migration of lead and nickel ions in groundwater-aquifers. Tests of batch operation have been conducted to determine the optimal conditions for removing Pb2+ ions, which were found to be 120 minutes of contact time, a pH of 5, 0.12 g of clay per 100 mL of solution, and an agitation of 250 rpm. These conditions resulted in a 90% removal efficiency for a 50 mg L−1 initial concentration of lead ions. To remove nickel ions with an efficiency of 80%, the optimal conditions were 60 minutes of contact time, a pH of 6, 12 g of clay per 100 mL of solution, and an agitation of 250 rpm. Several sorption models were evaluated, and the Langmuir formula was found to be the most effective. The highest sorption capacities were 1.75 and 137 mg g−1 for nickel and lead ions, respectively. The spread of metal ions was simulated using finite element analysis in the COMSOL multiphysics simulation software, taking into account the presence of a clay barrier. The results showed that the barrier creates low-discharge zones along the down-gradient of the barrier, reducing the rate of pollutant migration to protect the water sources. Validerad;2023;Nivå 2;2023-06-02 (hanlid);Funders: King Saud University, Saudi Arabia (RSP2023R407)

Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors). Validerad;2022;Nivå 2;2022-11-09 (johcin)

In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag (FS) and fly ash (FA) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR-PSO and SVR-GA, that are a hybridization of support vector regression (SVR) with improved particle swarm algorithm (PSO) and genetic algorithm (GA). Furthermore, the hybrid models (i.e., SVR-PSO and SVR-GA) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR-PSO has provided the highest accuracy than comparative models. For instance, the SVR-PSO during the testing phase provided fewer root mean square error RMSE with 1.386 MPa, a higher Nash-Sutcliffe model efficiency coefficient (NE) of 0.972, and lower uncertainty at 95% (U95) with 28.776%. On the other hand, the SVR-GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U95 with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS. Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies. Validerad;2022;Nivå 2;2022-03-25 (sofila);Funder: Al-Maarif University College

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome. Validerad;2022;Nivå 2;2022-04-21 (joosat)

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite's (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani's (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith's (1948) equations were cited in 9307 research studies, but the equations of Allen et al. (1998) were the subject of a significant number of citations from 23,000 publications. Based on application, PET is most often applied in the fields of hydrology, meteorology, and climatology, whereas RET is more frequently utilized in the fields of agronomy, agriculture, irrigation, and ecology. PET has been used to derive drought indices, whereas RET has been employed for single crop and dual crop coefficient approaches. This work examines and describes the ideas and methodologies, widely used equations, applications, and advanced approaches associated with PET and RET, and discusses future enhancements to increase the accuracy of ET calculation to attain accurate agricultural irrigation scheduling. The use of advanced tools such as remote sensing and satellite technologies, in addition to machine learning algorithms, will help to improve the accuracy of PET and RET estimates. Researchers will be able to distinguish between PET and RET in the future with the use of the study's results. Validerad;2022;Nivå 2;2022-09-08 (sofila)

Fluoride contamination in water is a key problem facing the world, leading to health problems such as dental and skeletal fluorosis. So, we used low-cost multifunctional tea biochar (TBC) and magnetic tea biochar (MTBC) prepared by facile one-step pyrolysis of waste tea leaves. The TBC and MTBC were characterized by XRD, SEM, FTIR, and VSM. Both TBC and MTBC contain high carbon contents of 63.45 and 63.75%, respectively. The surface area of MTBC (115.65 m2/g) was higher than TBC (81.64 m2/g). The modified biochar MTBC was further used to remediate the fluoride-contaminated water. The fluoride adsorption testing was conducted using the batch method at 298, 308, and 318 K. The maximum fluoride removal efficiency (E%) using MTBC was 98% when the adsorbent dosage was 0.5 g/L and the fluoride concentration was 50 mg/L. The experiment data for fluoride adsorption on MTBC best fit the pseudo 2nd order, rather than the pseudo 1st order. In addition, the intraparticle diffusion model predicts the boundary diffusion. Langmuir, Freundlich, Temkin, and Dubnin–Radushkevich isotherm models were fitted to explain the fluoride adsorption on MTBC. The Langmuir adsorption capacity of MTBC = 18.78 mg/g was recorded at 298 K and decreased as the temperature increased. The MTBC biochar was reused in ten cycles, and the E% was still 85%. The obtained biochar with a large pore size and high removal efficiency may be an effective and low-cost adsorbent for treating fluoride-containing water. Validerad;2022;Nivå 2;2022-10-18 (hanlid)

In the current study area (Faridabad, Gurugram, Ghaziabad, and Gautam Buddha Nagar), the aerosol concentration is very high, adversely affecting the environmental conditions and air quality. Investigating the impact of Land Use Land Cover (LULC) on Aerosol Optical Depth (AOD) helps us to develop effective solutions for improving air quality. Hence, the spectral indices derived from LULC ((Normalized difference vegetation index (NDVI), Soil adjusted vegetation index (SAVI), Enhanced vegetation index (EVI), and Normalized difference build-up index (NDBI)) with Moderate Resolution Imaging Spectroradiometer (MODIS) Multiangle Implementation of Atmospheric Correction (MAIAC) high spatial resolution (1 km) AOD from the years 2010-2019 (less to high urbanized period) has been correlated. The current study used remote sensing and Geographical Information System (GIS) techniques to examine changes in LULC in the current study region over the ten years (2010-2019) and the relationship between LULC and AOD. A significant increase in built-up areas (12.18%) and grasslands (51.29%) was observed during 2010-2019, while cropland decreased by 4.42%. A positive correlation between NDBI and SAVI (0.35, 0.27) indicates that built-up soils play an important role in accumulating AOD in a semi-arid region. At the same time, a negative correlation between NDVI and EVI (-0.24, -0.15) indicates the removal of aerosols due to an increase in vegetation. The results indicate that SAVI can play an important role in PM2.5 modeling in semi-arid regions. Based on these findings, urban planners can improve land use management, air quality, and urban planning. Validerad;2023;Nivå 2;2023-01-16 (joosat);Licens fulltext: CC BY License

Iraq has been suffering from decreasing Euphrates discharge due to the construction of dams within upstream countries and the use of surface irrigation systems. The country is facing a problem with meeting the increasing demand for water as a result of population growth and development in the industrial and agricultural sectors. Therefore, a simulation modelling was applied for western Iraq (Ramadi city as a case study) using the Water Evaluation and Planning System (WEAP) for the period 2018–2035. This research follows a four-step approach that involves: (i) evaluating the available water of the Euphrates River under declined water imports caused by the construction of dams in Turkey and Syria, (ii) assessing present and future water demands of the domestic, industrial, and agricultural sectors, (iii) improving water productivity (WP) by means of saving more water, (iv) estimating the economic returns under improved water use. The results showed that Iraq would face a serious problem in the coming years, represented by the limited storage of Haditha Dam, which is considered the strategic water storage site for the central and southern regions of Iraq. The study indicated the necessity of finding alternative sources of water supply by adopting new water management strategies to reduce the water deficit. Godkänd;2022;Nivå 0;2022-06-29 (sofila)

This experimental study concerns the elimination of fluoride from water using an electrocoagulation reactor having a variable flow direction in favour of increasing the electrolysing time, saving the reactor area, and water mixing. The detention time of the space-saver EC reactor (S-SECR) was measured and compared to the traditional reactors using an inert dye (red drain dye). Then, the influence of electrical current (1.5 ≤ δ ≤ 3.5 mA cm−2), pH of water (4 ≤ pH ≤ 10), and distance between electrodes (5 ≤ ϕ ≤ 15) on the defluoridation of water was analysed. The effect of the electrolysing activity on the electrodes' morphology was studied using scanning electron microscopy (SEM). Additionally, the operational cost was calculated. The results confirmed the removal of fluoride using S-SECR met the guideline of the World Health Organization (WHO) for fluoride levels in drinking water of ≤1.5 mg/L. S-SECR abated fluoride concentration from 20 mg/L to the WHO's guideline at δ, ϕ, pH, operational cost, and power consumption of 2.5 mA cm−2, 5 mm, 7, 0.346 USD m−3, and 5.03 kWh m−3, respectively. It was also found the S-SECR enhanced the detention time by 190% compared to the traditional reactors. The appearance of dents and irregularities on the surface of anodes in the SEM images proves the electrolysing process.

Although heavy metals are typically found in trace levels in natural waterways, most of them are hazardous to human health and the environment, even at extremely low concentrations. Nanotechnology and nanomaterials have gained great attention among researchers as a sustainable route to addressing water pollution. Researchers focus on developing novel nanomaterials that are cost-effective for use in water/wastewater remediation. A wide range of adsorbed nanomaterials have been fabricated based on different forms of natural materials, such as carbonaceous nanomaterials, zeolite, natural polymers, magnetic materials, metal oxides, metallic materials, and silica. Hence, this review set out to address the ability of various synthesized nanoadsorbent materials to remove different heavy metal ions from water and wastewater and to investigate the influence of the functionalization of nanomaterials on their adsorption capacity and separation process. Additionally, the effect of experimental variables, such as pH, initial ion concentration, adsorbent dose, contact time, temperature, and ionic strength, on the removal of metal ions has been discussed. Validerad;2022;Nivå 2;2022-09-26 (joosat)

Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively. Validerad;2022;Nivå 2;2022-09-06 (johcin)

The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. Validerad;2022;Nivå 2;2022-11-14 (hanlid)

Constructing a static reservoir model or a 3D geological model is a widely used modern tool to employ subsurface information expose the setting and properties of hydrocarbon reservoirs. The current study attempts to build a 3D reservoir model of the Yamama Formation (an important oil reservoir in Southern Iraq), from digital logs data and drilling information. This would lead to a better understanding of the relationships between different reservoir elements, then expose many characteristics of the reservoir, such as the facies distribution and petrophysical properties. The data of the Yamama Formation (Early Cretaceous Carbonates) were taken from four wells of Gharraf Oilfield, Southern Iraq (GA-1, GA-2, GA-3, and GA-4). The adopted modeling approach consists of a series of steps starting with a preliminary analysis of data, followed by interpretation of these data, and terminated by geostatistical methods for building the structural model. The modeling was assisted by defining the top of each layer detected by wireline logs and final well reports of each studied well. The results of the study encompass four microfacies distributed between Inner Ramp and Mid Ramp environments. The followed 3D geological modeling scheme was capable to visualize the distribution of petrophysical properties, and the classification of Yamama Formation into several layers or reservoir units (Y1 to Y5), and thirteen subdivisions (minor units). The model could also define the places where effective porosity was enhanced. The process of Pillar Gridding, a step within the modeling, showed that the Yamama Formation is composed of two domes. The constructed model was helpful, based on the evaluation of petrophysical parameters, then to point out the most important reservoir zone (Zone Y3_top) that has Effective Porosity of (13.6%) and water saturation of around (10.6%). Godkänd;2022;Nivå 0;2022-06-02 (johcin)

Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations. Validerad;2022;Nivå 2;2022-11-15 (hanlid)

India’s water resources are under tremendous pressure due to elevated demand for various purposes. The over-exploitation of these valuable resources has resulted in an imbalance in the watershed ecology. The application of spatial analysis tools in studying the morphological behaviour of watersheds has increased in recent decades worldwide due to the accessibility of the geospatial database. A morphometric analysis of a river basin is vital to determine the hydrological behaviour to develop effective management. Under the current study, morphological behaviour of Nand Samand catchment in the hard rock region was evaluated employing remote sensing (RS) and geographical information system (GIS) tools. The Nand Samand catchment (Rajasthan State, India) has an area of 865.18 km2 with the highest and lowest elevations of 1318 m and 570 m above mean sea level, respectively. This study utilises a 30 m high-spatial-resolution ASTER imagery digital elevation model for delineating the catchment. The drainage network is assessed using a GIS method, and morphometric parameters like linear, areal, and relief aspects were calculated. Results were obtained for parameters viz., basin length of 82.66 km, constant channel maintenance equal to 0.68 km, stream frequency of 2.11 km−2, drainage density of 1.48 km−1, and length overflow of 0.34 km. Form factor of 0.13, and the circulatory ratio of 0.28 showed that an elongated shape characterises the study area. The results would help understand the relationship between hydrological variables and geomorphological parameters for better decision-making. The techniques used could effectively help to perform better drainage basin and channel network morphometric analyses. The found morphometric characteristics will be helpful in understanding the Nand Samand catchment and similar areas in India in order to better guide the decision-makers in providing adequate policy to the development of the region Validerad;2022;Nivå 2;2022-02-11 (joosat)

Hydrological soil group is essential to soil information for several fields of modeling and applications. This information can affect suitable environmental, agricultural, and hydrological development. Laboratory analysis for soil sampling cannot efficiently provide the needed information because these analyses are commonly costly, time-consuming, and limited in retrieving the temporal and spatial variability. In this context, remote sensing is now solid to offer meaningful spatial data for studying soil characteristics on various spatial scales utilizing the different spectral reflectance. For this study, the integration of Geographic Information System (GIS) remote sensing data and survey data with the Artificial Neural Network (ANN) were used to generate a hydrological soil group map and to infer spatial patterns of soils across complete area converges for Alghadaf Wadi in the Western Desert of Iraq. The generated soil information was tested based on the sand, silt, and clay content. The testing result indicated that the differences between actual and predicted values to determine soil classes are agreed well. Therefore, this method is vital for mapping and monitoring soil texture by providing timely, fast repetitive data and relatively cheap. Validerad;2022;Nivå 1;2022-11-08 (joosat)

The DRAINMOD model is a superior tool used to predict the changes in farmland water balance under different agricultural drainage layouts, fields, weather conditions, and management practices. In the present study, we assessed the sensitivity of the DRAINMOD predictions in farmland water balance to the time step (hourly or daily) in daily evapotranspiration (ET0 ) computations for 12-hectares of farmland located at the lower reaches of the Yangtze River basin. The model was calibrated and validated and then was applied twice under two sets of daily ET0 values, computed using the standardized ASCE Penman–Monteith model (one using the hourly time step (HTS) and the other using the daily time step (DTS)). Regarding daily computed ET0 values, results show that abrupt diurnal changes in the weather always result in significant differences between daily ET0 values when computed based on DTS and HTS. DRAINMOD simulations show that such differences between daily computed ET0 values affected the model’s predictions of the “water fate” in the study area; e.g., adopting HTS rather than DTS resulted in a 4.8% increase, and a 3.1% and 1% decrease in the models’ cumulative predictions of runoff, drainage, and infiltration, respectively. Therefore, for a particular study area, it is critical to pay attention when deciding the best time step in ET0 computations to ensure accurate DRAINMOD simulations, thereby ensuring better utilization of agricultural water alongside high agricultural productivity. Validerad;2022;Nivå 2;2022-02-18 (joosat)

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluation of landslide susceptibility models and maps. The landslide data of 91 locations were randomly divided into two parts in the ratio of 70 : 30 using GIS software that is 70% data was used for training the models and 30% data was used for testing and validating the models. Performance of the applied models was evaluated using area under the AUC (area under the curve) ROC (receiver operating characteristics) curve. Results indicated that the WOE model is having better accuracy (AUCWOE = 68.75%) than the SE model (AUCSE = 52.17%) in the development of landslide susceptibility maps. Hence, WOE model can be used for the development of accurate landslide susceptibility maps which can provide useful information to decision maker and policy planner in better development of landslide prone areas. Validerad;2022;Nivå 2;2022-04-19 (hanlid);Funder: University of Transport Technology, Hanoi, Vietnam

Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre-processing and artificial neural network (ANN) optimised with constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al-Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R2 = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters. Validerad;2022;Nivå 2;2022-11-29 (hanlid)

For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms. Validerad;2022;Nivå 2;2022-05-06 (joosat)

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies. Validerad;2022;Nivå 2;2022-11-07 (joosat)

The land surface temperature (LST) pattern is regarded as one of the most important indicators of the environmental consequences of land use/land cover change. The possible contribution of land surface to the warming phenomenon is being investigated by scientists across the world. This research focuses on variations in surface temperature and urban heat islands (UHIs) over the course of two seasons, i.e., winter and summer. Using remotely sensed datasets and geospatial techniques, an attempt was made to analyze the spatiotemporal variation in urban heat islands (UHIs) and its association with LULC over Chandigarh from 2000 to 2020. The Enhanced Built-up and Bareness Index (EBBI), Dry Built-up Index (DBI), and Dry Bare-Soil Index (DBSI) were used to identify built-up areas in the city. The results revealed an increase of 10.08% in BA, whereas the vegetation decreased by 4.5% over the study period, which is in close agreement with the EBBI, DBI, and DBSI assessments. From 2000 to 2020, the UHI intensities increased steadily in both the summer and winter seasons. Dense built-up areas such as the industrial unit of the city possessed the highest UHIindex (>0.7) values. Validerad;2022;Nivå 2;2022-09-22 (sofila)

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed. Validerad;2022;Nivå 2;2022-07-05 (sofila);Funder: , G.B. Pant University of Agriculture and Technology, India; Gola Barrage gauge station Haldwani–Kathgodam, India; Portuguese Foundation for Science and Technology (PTDC/CTA-OHR/30561/2017, WinTherface)

Mixed data is a combination of continuous and categorical variables and occurs frequently in fields such as agriculture, remote sensing, biology, medical science, marketing, etc., but only limited work has been done with this type of data. In this study, data on continuous and categorical characters of 452 genotypes of cotton (Gossypium hirsutum) were obtained from an experiment conducted by the Central Institute of Cotton Research (CICR), Sirsa, Haryana (India) during the Kharif season of the year 2018–2019. The machine learning (ML) classifiers/models, namely k-nearest neighbor (KNN), Classification and Regression Tree (CART), C4.5, Naïve Bayes, random forest (RF), bagging, and boosting were considered for cotton genotypes classification. The performance of these ML classifiers was compared to each other along with the linear discriminant analysis (LDA) and logistic regression. The holdout method was used for cross-validation with an 80:20 ratio of training and testing data. The results of the appraisal based on hold-out cross-validation showed that the RF and AdaBoost performed very well, having only two misclassifications with the same accuracy of 97.26% and the error rate of 2.74%. The LDA classifier performed the worst in terms of accuracy, with nine misclassifications. The other performance measures, namely sensitivity, specificity, precision, F1 score, and G-mean, were all together used to find out the best ML classifier among all those considered. Moreover, the RF and AdaBoost algorithms had the highest value of all the performance measures, with 96.97% sensitivity and 97.50% specificity. Thus, these models were found to be the best in classifying the low- and high-yielding cotton genotypes. Validerad;2022;Nivå 2;2022-10-24 (hanlid)

Water resources are of great importance in the world's agriculture, especially regarding the scarcity of these resources. That calls for attention appropriate for analysis, study, and research in all issues and aspects that would contribute to the development and maintenance of those resources and achieve the maximum possible levels of quality and efficiency of use. The study was conducted during the fall season of 2020 to study groundwater quality (well water) in the city of Al-Qaim of Anbar Province to explain its suitability for agricultural exploitation in the region. The study included seven sites in Al-Qaim (Rtemi, Medicis, Eastern Akash, Okesha, Sawab, Albu-Hayat, and Al-Karah) to assess the validity of irrigation. The pH, Electrical conductivity (EC), positive ions (K+, Na+, Mg2+, Ca2+), and negative ions (HCO3, Cl-2, SO4) and CO3 were measured. The results showed that the studied well water is acceptable for irrigation purposes in terms of pH value, as for the electrical connection, it was six wells within the class (C2), which is adequate for irrigation for medium-salty crops, except for one well, which is a fine well within class C3 suitable for irrigation of high salinity crops. The total hardness values were low and did not pose any risk; as for the positive and negative ions, they were within the permissible limits within the specified classifications. By measuring the positive ions, the Sodium Adsorption Ratio (SAR) value was calculated and determined to be within the class S1, which means that the groundwater in the region is suitable for irrigation. Validerad;2022;Nivå 1;2022-04-27 (sofila)

The design of dams requires comprehensive studies to ensure the safety and feasibility of these important engineering projects, as any possible failure case may lead to considerable losses in human life and properties. Specifically, analyses should be performed to evaluate seepage, slope stability, and soil liquefaction of large earth dams. In this study, numerical modeling, based on finite element methods, was used to analyze seepage, slope stability, and liquefaction of Makhoul Dam which is a large zoned dam, currently under construction on Tigris River in the north of Iraq. Earthquake shakings impose additional hysteric and short-term loads that may lead to dam failure due to high pore water pressure, piping, and soil liquefaction. Therefore, the dynamic stability of the dam and soil liquefaction were also evaluated, as a result of applying an earthquake shaking to the dam. For the static condition, the dam was safe against internal erosion and slope failure, as the calculated value of the safety factor was greater than the allowable value. However, the results obtained from the dynamic analysis indicated that a possible earthquake, with an acceleration of 0.38g and 10 seconds period, led to upstream slope failure, a relative displacement as high as 2 meters at the dam crest, and soil liquefaction at the upstream slope. As discussed herein, dam redesign or geotextile reinforcement may be considered to reduce the effects of earthquakes on the dam. Validerad;2022;Nivå 1;2022-09-08 (sofila)

The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels, dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory. Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are used to validate and evaluate the accuracy of the model. The results show that the TLBO-ANN model is an effective tool in predicting correctly the k-value (R = 0.905) of soil for the consideration in the design of structures founded on the soil. Validerad;2022;Nivå 2;2022-03-21 (hanlid)

Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended. Validerad;2022;Nivå 2;2022-01-12 (johcin)

Climate and land-use change are important factors in the hydrological process. Climatic and anthropic changes have played a crucial role in surface runoff changes. The objective of this research was to apply land-use change and future climate change to predict runoff change in the Pak Phanang River Basin. The Cellular Automata (CA)-Markov model was used to predict the land-use change, while the climate data from 2025 to 2085 under RPC2.6, RPC4.5, and RPC8.5 were generated using the MarkSim model. Additionally, the Soil and Water Assessment Tool (SWAT) combined land-use change and the generated meteorological data to predict the runoff change in the study area. The results showed that the annual runoff in the area would increase in the upcoming year, which would affect the production of field crops in the lowland area. Therefore, a good water drainage system is required for the coming years. Since the runoff would be about 50% reduced in the middle and late 21st century, an agroforestry system is also suggested for water capturing and reducing soil evaporation. Moreover, the runoff change’s overall impact was related to GHG emissions. This finding will be useful for the authorities to determine policies and plans for climate change adaptation in the Malay Peninsula. Validerad;2022;Nivå 2;2022-07-26 (hanlid);Funder: Walailak University, Thailand (WU63245)

The standardized precipitation index (SPI) is one of the most widely used indices for characterizing and monitoring drought in various regions. SPI's applicability has regional and time-scale constraints when it observes in several homogeneous climatic regions with similar characteristics. It also does not provide sufficient knowledge about precipitation deficits and the spatiotemporal evolution of drought. Therefore, a new method, the regional spatially agglomerative continuous drought probability monitoring system (RSACDPMS), is proposed to obtain spatiotemporal information and monitor drought characteristics more expeditiously. The proposed framework uses spatially agglomerative precipitation (SAP) and copulas’ functions to continuously monitor the drought probability in the homogenous region. The RSACDPMS is validated in the region of the Northern area of Pakistan. The outcomes of the current study provide a better quantitative way to obtain appropriate information about precipitation deficits and the spatiotemporal evolution of drought. Validerad;2022;Nivå 2;2022-02-03 (johcin);Funder: King Khalid University (RGP.2/4/43)

Lysimetric and eddy covariance techniques are commonly used to directly estimate actual crop evapotranspiration (ETa). However, these technologies are costly, laborious, and require skills which make in situ ET estimation difficult, particularly in developing countries. With this in mind, an attempt was made to determine ETa and stagewise crop coefficient (Kc) values of transplanted puddled rice using a modified non-weighing paddy lysimeter. The results were compared to indirect methods, viz., FAO Penman–Monteith and pan evaporation. Daily ETa ranged from 1.9 to 8.2 mmday−1, with a mean of 4.02 ± 1.35 mmday−1, and their comparison showed that the FAO Penman–Monteith equation performed well for the coefficient of determination (R2 of 0.63), root mean squared error (RMSE = 0.80), and mean absolute percentage error (MAPE = 13.6 %), and was highly correlated with ETa throughout the crop season. However, the pan evaporation approach was underestimated (R2 of 0.24; RMSE = 0.98; MAPE = 22.13%) due to a consistent pan coefficient value (0.71), vegetation role and measurement errors. In addition, actual Kc values were obtained as 1.13 ± 0.13, 1.27 ± 0.2, 1.23 ± 0.16, and 0.93 ± 0.18 for the initial, crop development, mid-season, and end-season stages, respectively. These estimated crop coefficient values were higher than FAO Kc values. Statistical analysis results revealed that the overall stagewise-derived average Kc values were in line with FAO values, but different from the derived pan Kc values, although found insignificant at a 5% significance level. In addition, water productivity and agro-meteorological indices were derived to evaluate the cultivar performance in this experiment. Therefore, such a methodology may be used in the absence of weighing lysimeter-derived Kc values. The derived regional Kc values can be applied to improve irrigation scheduling under similar agro-climatic conditions. Validerad;2022;Nivå 2;2022-11-16 (joosat)

Mosul and Haditha dams are two large earthfill dams in Iraq constructed on the Tigris and Euphrates rivers, respectively. The two dams were constructed almost at the same period in the mid-eighties of the last century. Both dams suffer from karstification within their foundations. In the former, however, the problem is more severe than the latter. Mosul Dam was constructed on karstified gypsum and limestone beds; different grouting techniques were used, but the works in grouting are still going on to keep the dam as safe as possible as the sealing of the foundation is hampered by the type of geology. Haditha Dam was constructed on karstified limestone and gypsum rocks also. To avoid the effect of the karstification on the safety of this dam, an exceptionally long grout curtain was constructed as foundation treatment work. The length of the grout curtain extended under the earthfill dam and the concrete structures in the river channel and extended beyond the abutments forming left and right sides extensions to cut off water percolation around the dam which could cause the formation of sinkholes. The depths of all parts of the curtain varied following the karstification zones and intensities. The details of both dams are discussed using updated data and relying on the experience of the authors. The current status of both dams is also discussed with some recommendations to keep both dams as safe as possible. Godkänd;2022;Nivå 0;2022-01-12 (johcin)

Groundwater in the Touggourt region—or as its named, Oued Righ—in southeastern Algeria, is the only source of irrigation. To assess its suitability for agricultural purposes, we collected 72 samples from wells at this region, physical and chemical measurements were carried out for each water sample, and calculations of the sodium adsorption ratio (SAR), permeability index (PI), soluble sodium percent (SSP), residual sodium carbonate (RSC), magnesium hazard ratio (MHR) and Kelley’s ratio (KR) were carried out, as these indices are often used to assess the suitability of groundwater for irrigation uses. Based on the irrigation water quality index (IWQI) values, a spatial distribution map for each parameter using the inverse interpolation technique (IDW) was produced by Geographical Information System (GIS). According to the IWQI map, about 35% of the water samples analyzed fall into the Severe Restriction category (SR), making it unsuitable for irrigation under normal circumstance. Again, the remaining 65% of the groundwater has a high restriction (HR) for use. Groundwater in the study area could be used for irrigation in highly permeable soils where salt-tolerant crops are grown. Adequate drainage and continuous monitoring of water quality are recommended. Validerad;2022;Nivå 2;2022-07-20 (sofila);Funder: King Khalid University (grant no. RGP. 2/246/43)