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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)

Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98). Validerad;2022;Nivå 2;2022-10-12 (joosat)

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)

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)

The agriculture sector plays a vibrant role in the economic prosperity of advanced and developing countries. It is a crucial source of revenue for the majority of the population. Nevertheless, unfortunately, in Pakistan, the share of the agricultural sector in Gross Domestic Product (GDP) is gradually declining. Therefore, comprehensive strategies and actions need to be developed and implement to enhance the agricultural productivity of Pakistan. In this study, an attempt has been made to examine the crop yield revenue of Punjab, Pakistan, by ranking the districts according to their contribution to the agricultural GDP of Pakistan's economy. A Multi-Criteria Decision Making (MCDM) technique, namely, characteristic objects method (COMET), which is entirely free of the rank reversal paradox, is used for this purpose. However, to make a fair comparison, in this research, a comprehensive framework is proposed to normalize the crop yield revenue of Punjab under probabilistic nature. The proposed framework is applied to various districts of Punjab, Pakistan, from 1992 to 2019. It is concluded that Jhang, Faisalabad, and Rahim Yar Khan (RYK) are the highest-ranked districts, while Nankana Sahib, Rawalpindi, and Islamabad are the lowest-ranked districts of Punjab, Pakistan, according to their contribution to the agricultural GDP of Pakistan's economy. Outcomes associated with this research would be helpful to build precise and accurate budget allocation policies. Validerad;2022;Nivå 2;2022-03-21 (hanlid);Part of special issue: Multivariate and Big Data Modeling and Related Issues

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)

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)

In global warming contexts, continuous increment in temperature triggers several environmental, economic, and ecological challenges. Its impacts have severe effects on energy, agriculture, and socioeconomic structure. Moreover, the strong correlation between temperature and dynamic changing of rainfall patterns greatly influences the natural cycles of water resources. Therefore, it is necessary to examine the spatiotemporal variation of precipitation to improve precipitation monitoring systems. Thereby, it helps to make future planning for flood control and water resource management. Considering the importance of the spatiotemporal assessment of precipitation, the current study provides a new method: regional contextual precipitation concentration index (RCPCI) to analyze spatial-temporal patterns of annual rainfall intensities by reconnaissance the precipitation concentration index (PCI) in the global warming context. The current study modifies the existing version of PCI by propagating the role of temperature as auxiliary information. Further, based on spatial and nonspatial correlation analysis, the current study compares the performance of RCPCI and PCI for 45 meteorological stations of Pakistan. Tjøstheim’s coefficient and the modified t-test are used for testing and estimating the spatial correlation between both indices. In addition, the Poisson log-normal spatial model is used to assess the spatial distribution of each rainfall pattern. Outcomes associated with the current analysis show that the proposed method is a good and efficient substitute for PCI in the global warming scenario in the presence of temperature data. Therefore, to make accurate and precise climate and precipitation mitigation policies, the proposed method may incorporate uncovering the yearly pattern of rainfall. Validerad;2022;Nivå 2;2022-08-05 (hanlid);Part of special issue: Multivariate and Big Data Modeling and Related Issues

Water pollution reduces the availability of fresh water, especially in arid areas suffering from water stress, and also adversely affects soil, vegetation and environmental processes. Wastewater treatment processes aim to reduce environmental degradation and increase water availability by improving the quality of wastewater to a standard suitable for irrigation. This paper compares the performance of three wastewater treatment processes: (i) aerated lagoon (AL), (ii) activated sludge (AS), and (iii) constructed wetland (plant beds, PB) under the arid climate of Algeria. The statistical analysis focused on the comparison between the removal rates of the physical (SS) and biological pollution (BOD5 and COD) parameters in the three stations during 8 years of operation. Obtained results show that the maximum removal rates were observed in the AS process and the minimum were in the AL process. The comparison between the removal rates for a given parameter has shown that there is a significant difference between the AL process on the one hand and the AS and PB processes on the other hand. For the last two processes, AS and PB, there is a difference, but it is not statistically significant. For the values of the parameters of wastewater leaving the three systems, results showed that there is a seasonal variation in the average values of the parameters (temperature effect) and that with the exception of orthophosphate, the values recorded are, for the most part, below the values of Algerian discharge standards, WHO standards and FAO standards. Validerad;2023;Nivå 2;2023-01-01 (joosat);Licens full text: CC BY license

Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications. Validerad;2022;Nivå 2;2022-10-04 (hanlid)

Juniperus macropoda is the only tree species of a cold desert ecosystem that is experiencing high anthropogenic pressure and has a poor regeneration status due to harsh environmental conditions. Due to the limited distribution of Juniperus macropoda in this region, the species have remained largely unexplored in terms of understanding the distribution pattern along the elevation and soil fertility gradients. Therefore, the current research was carried out along the elevational gradient, starting from the base line at 3000 m above sea level (m asl) asl with an elevational plot distance of 180 m. The study revealed that the average density of J. macropoda declined gradually from the first elevation range, i.e., 3000–3180 m asl onward, and extended up to the elevation range of 3900–4080 m asl. However, the average seedling and sapling densities were highest at mid-elevation and extended up to an elevation range of 4080–4260 m asl. The J. macropoda population formed a reverse J-shaped structure only up to 3540–3720 m asl. The maximum total biomass and carbon density were recorded in the lowest elevational range, and decreased subsequently. The primary soil nutrients under study decreased sharply along the elevational gradient. Seedling, sapling and tree distributions had a significantly positive relationship (p < 0.05) with available N, P, K, SOC, silt and clay contents and were negatively correlated (p < 0.05) with sand contents. The outcome of the study will form the basis for devising a plan for the management and conservation of J. macropoda forests. Validerad;2022;Nivå 2;2022-10-04 (sofila)

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas. Validerad;2022;Nivå 2;2022-09-02 (hanlid)

Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield. Validerad;2022;Nivå 2;2022-03-03 (sofila)

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. Validerad;2022;Nivå 2;2022-10-03 (hanlid);Funder: Al-Maarif University College, Ramadi, Iraq

Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, andmachine learning (ML), along themain road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performancematrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelumroad is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslideprone zones along road corridors. Validerad;2023;Nivå 2;2023-01-19 (joosat);Licens fulltext: CC BY License

The Climate change is a global issue affecting different parts of our planet where we are living. However, the reasons of climate change and consequences differ at different parts too. In Iraq, including the Kurdistan Region, the reasons for the climate change are due to man-made and natural effects, where the rates of CO2 emission and those of other greenhouse gasses are increasing drastically, besides the global warming, decrease in the amount of water income in rivers and streams from Turkey and Iran, decrease of rain and snow fall, increase of population. All these have direct impact on the climate and accordingly the consequences are coming harsher and seriously effective on the daily life of the people. In this research, different man-made and natural effects, which directly affect the climate change are presented and described. Moreover, predictions and recommendations are given to decrease the consequences of the climate change in Iraq among them the status of awareness is one of the main reasons to climate change, besides the global warming. Godkänd;2022;Nivå 0;2022-06-20 (sofila);DOI för värdpublikation: 10.17406/GJHSS

Biochar is a carbon-based substance made by the pyrolysis of organic waste. The amount of biochar produced is determined by the type of feedstock and pyrolysis conditions. Biochar is frequently added to the soil for various reasons, including carbon sequestration, greenhouse gas mitigation, improved crop production by boosting soil fertility, removing harmful contaminants, and drought mitigation. Biochar may also be used for waste management and wastewater treatment. Biochar’s various advantages make it a potentially appealing instrument material for current science and technology. Although biochar’s impacts on soil chemical qualities and fertility have been extensively researched, little is known about its impact on enhancing soil physical qualities. This review is intended to describe biochar’s influence on some crucial soil physical and hydrological properties, including bulk density of soil, water holding capacity, soil porosity, soil hydraulic conductivity, soil water retention, water repellence–available plant water, water infiltration, soil temperature, soil color, and surface albedo. Therefore, we propose that the application of biochar in soils has considerable advantages, and this is especially true for arable soils with low fertility. Validerad;2022;Nivå 2;2022-09-06 (johcin)

The present work is represented by the derivation of mathematical model and solving the model analytically using the method of separation of variables to describe the migration of the contaminant metal ions through a column packed with bed of permeable reactive barrier (PRB). The validity of the solution can be evaluated through the simulation of cadmium and chromium ions using scrap iron and/or aluminum by-products in the form of wastes that if not utilized to treat waste by waste can impose further burden over the ecosystem. Breakthrough curves proved that the increase of metal ions velocity will decrease the capturing of the ions; therefore, the distribu-tion coefficient and the retardation factor also decrease. Furthermore, the increase of barrier depth will increase the longevity of PRB because this will delay the migration of contaminant. A mathematical model has acceptable ability in the representation of experimental measurements with Nash-€“Sutcliff efficiency coefficients greater than 0.98. The longevity of the PRB was estimated for the field scale to be 210 and 250 d to produce contaminant effluent beyond 100 cm barrier matrix within the environmental permissible concentrations. Although groundwater velocity is highly variable, a proposed velocity of 0.25 cm/min which is assumed to be analogous to the groundwater velocity has revealed prolonged longevity of 7.02 y for the capture of chromium. Validerad;2022;Nivå 2;2022-08-18 (hanlid);Funder: Taif University (grant no. TURSP-2020/49)

The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity. Validerad;2022;Nivå 2;2022-07-01 (joosat)

Groundwater vulnerability assessment is an effective tool in the joint management of transboundary groundwater, especially in developing countries where data is scarce, monitoring networks are insufficient, and water is both a cause and a target of conflicts. The Jezira Tertiary Limestone Aquifer Transboundary System (JTLATS) region, which Syria, Iraq, and Turkey share, gives a clear description of the shared water problem in developing countries with arid and semiarid environments. In this study, a comprehensive multidisciplinary Groundwater Vulnerability Index (GVI) was developed as a distributed composite index to assess the groundwater vulnerability in JTLATS by combining different environmental and political socioeconomic datasets and models for three periods between 2003 and 2017. The JTLATS was categorized into five zones: very low, low, moderate, high, and very high vulnerability. The results showed a low vulnerability in the southern regions of the aquifer. In comparison, the areas with high vulnerability are primarily spread in the northern and western parts of the JTLATS and along the Euphrates river. The results showed an increase in the percentage of areas with high vulnerability from 10.45% in (2003-2007) to 13.42% and 20.57% of the aquifer area in (2007-2011) and (2011-2017), respectively. The groundwater vulnerability in the aquifer increased with the spread of political instability in both Syria and Iraq and the increase in cultivated areas in Turkey. Godkänd;2023;Nivå 0;2023-01-01 (hanlid)

Existing bond-slip (τ-s) relations for fibre-reinforced polymer (FRP)-steel joints employ different shapes and mathematical expressions, inferring that their predictions of failure load and mode, and other interface properties, might be inconsistent or inaccurate. In this study, predictions of four widely used τ-s relations are evaluated using a large experimental database of 78 double-lap FRP-steel specimens. To facilitate the evaluation process, a robust finite element (FE) model is developed for each test, implementing data from either of the existing τ-s relations to define the FRP-steel interface. Comparisons between test and FE results indicated that the existing τ-s models were unable of predicting the ultimate load (Pu) and effective bond length (Leff) of FRP-steel joints, or the relation between Pu and bond length and that between Leff and FRP modulus (Ef). A new τ-s model is developed based on an inverse FE simulation, comparison with experimental results, and regression analysis. It considers the effects of Ef, the type of FRP reinforcement (sheet or plate), and applies to both linear and nonlinear adhesives. The model predictions were validated by comparing with results from small bond tests and large FRP-strengthened steel beams tested under bending, yielding excellent results for Pu, failure mode, and all other interfacial properties. Validerad;2022;Nivå 2;2022-07-22 (sofila)

Drought is a complex phenomenon that occurs due to insufficient precipitation. It does not have immediate effects, but sustained drought can affect the hydrological, agriculture, economic sectors of the country. Therefore, there is a need for efficient methods and techniques that properly determine drought and its effects. Considering the significance and importance of drought monitoring methodologies, a new drought assessment procedure is proposed in the current study, known as the Maximum Spatio-Temporal Two-Stage Standardized Weighted Index (MSTTSSWI). The proposed MSTTSSWI is based on the weighting scheme, known as the Spatio-Temporal Two-Stage Standardized Weighting Scheme (STTSSWS). The potential of the weighting scheme is based on the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and the steady-state probabilities. Further, the STTSSWS computes spatiotemporal weights in two stages for various drought categories and stations. In the first stage of the STTSSWS, the SPI, SPEI, and the steady-state probabilities are calculated for each station at a 1-month time scale to assign weights for varying drought categories. However, in the second stage, these weights are further propagated based on spatiotemporal characteristics to obtain new weights for the various drought categories in the selected region. The STTSSWS is applied to the six meteorological stations of the Northern area, Pakistan. Moreover, the spatiotemporal weights obtained from STTSSWS are used to calculate MSTTSSWI for regional drought characterization. The MSTTSSWI may accurately provide regional spatiotemporal characteristics for the drought in the selected region and motivates researchers and policymakers to use the more comprehensive and accurate spatiotemporal characterization of drought in the selected region. Validerad;2022;Nivå 2;2022-05-02 (joosat);Funder: Deanship of Scientific Research at King Saud University (1435-075)

This study aims to evaluate the hydro-chemical characteristics of Ouargla, Algeria basin groundwaters harvested from the Mio Pliocene aquifer. The study covered 70 samples; the physical parameters, potential of hydrogen (pH), and electrical conductivity EC μS.cm−1 were determined in situ, using a multiparameter; the laboratory analysis included dry residuals DR (mg/L), calcium Ca2+ (mg/L), magnesium Mg2+ (mg/L), sodium Na+ (mg/L), potassium K+ (mg/L), bicarbonates HCO3− (mg/L), sulfates SO42− (mg/L), and chloride Cl− (mg/L). The piper diagram shows that the Ouargla basin ground waters divided into two facies, sodic chlorinated in 93% and sodic sulphated in 7% of samples. The United States Salinity Laboratory Staff (USSL) diagram was used to detect the suitability of groundwater in irrigation where the results show that the groundwater was classed into two classes, poor water (C4 S4) and bad water (C4 S4). Furthermore, indices such as the Kelly index (KI), sodium adsorption ratio (SAR), sodium solubility percentage (Na%), and magnesium hazards (MH) confirm the negative effect of groundwater on soil permeability in 96%, 80%, 89%, and 53% of samples. The permeability index (PI) shows that the analyzed samples were considered as doubtful (71%) and safe (29%), otherwise there is no risk related to residual sodium carbonate (RSC). The geo-spatial distribution of deferent indices shows that all the study area has poor groundwater for irrigation, except the south-west part, where the groundwaters of this sub-area do not form a problem related to RSC. Validerad;2022;Nivå 2;2022-08-09 (hanlid);Funder: Deanship of Scientific Research at King Khalid University, Saudi Arabia (RGP.2/246/43)

Reference evapotranspiration (ETo) plays an important role in agriculture applications such as irrigation scheduling, crop simulation, water budgeting, and reservoir operations. Therefore, the accurate estimation of ETo is essential for optimal utilization of available water resources on regional and global scales. The present study was conducted to estimate the monthly ETo at Nagina (Uttar Pradesh State) and Pantnagar (Uttarakhand State) stations by employing the three ML (machine learning) techniques including the SVM (support vector machine), M5P (M5P model tree), and RF (random forest) against the three empirical models (i.e., Valiantzas-1: V-1, Valiantzas-2: V-2, Valiantzas-3: V-3). Three different input combinations (i.e., C-1, C-2, C-3) were formulated by using 8-year (2009–2016) climatic data of wind speed (u), solar radiation (Rs), relative humidity (RH), and mean air temperature (T) recorded at both stations. The predictive efficacy of ML and the empirical models was evaluated based on five statistical indicators i.e., CC (correlation coefficient), WI (Willmott index), EC (efficiency coefficient), RMSE (root mean square error), and MAE (mean absolute error) presented through a heatmap along with graphical interpretation (Taylor diagram, time-series, and scatter plots). The results showed that the SVM-1 model corresponding to the C-1 input combination outperformed the other ML and empirical models at both stations. Moreover, the SVM-1 model had the lowest MAE (0.076, 0.047 mm/month) and RMSE (0.110, 0.063 mm/month), and highest EC (0.995, 0.999), CC (0.998, 0.999), and WI (0.999, 1.000) values during validation period at Nagina and Pantnagar stations, respectively, and closely followed by the M5P model. Consequently, the ML model (i.e., SVM) was found to be more robust, and reliable in monthly ETo estimation and can be used as a promising alternative to empirical models at both study locations. Validerad;2022;Nivå 2;2022-05-11 (sofila)

The present work analyzed the yield, water utilize efficiency and water prod-uctivity for carrot Crop under climate change effects. In this research, river water was used with irrigation ratios with drain water to irrigate the carrots (Daucus carota L.) crop during the winter growing season 2021-2022 in free field and utilizing furrow irrigation method with calculation the water use efficiency and water productivity to three sites. The study was conducted in the three sites in the Babil province in Town of Sadat-AlHindya reached 80 km from Baghdad city. Site 1 was used 33% drain water and 67% river water while site 2 was used 83% drain water and 17% river water but the site 3 was used 100% river water. The reduction of yield in site 1 was 17.3% and in site 2 was 75%. The reducing of WUE in site 1 was 23% and in site 2 was 77%. The decrease of WP in site 1 was 22.98% and in site 2 was 82.28%. The value of water stress coefficient (Ks) because irrigation by water salanity and soil salanty was 0.83, 0.78 and 0.92 in site 1, site 2 and site 3, respectively, water salanity and soil salanty reduction in productivity by 9%, 14% and 7% in production of site 1, site 2 and site 3, respectively in every irrigating. Validerad;2022;Nivå 1;2022-10-25 (hanlid)

Erbil city is constructed in a flat plain with moderate gradient increasing northwards, the plain is dissected by tens of ephemeral wadis. The maximum elevation in the high lands NE of Erbil city is 1062 m (a.s.l.), whereas the elevation of the center of Erbil city is 420 m (a.s.l.). The average gradient from the highest part to the center of the city is 2.15%, increasing towards northeast to reach 4.79%. The mean annual rainfall is 365 mm, while the average monthly rainfall is about 24.42 mm. The site of the city is mainly covered by alluvial fan sediments. Towards northeast, however, the exposures of the Bia Hassan Formation, which consists of alternation of conglomerate and claystone cover considerable areas (82 km2) and form the high lands NE of Erbil city. The exposed rocks are thick claystone alternating with coarse conglomerate. Two very heavy torrential rain events have caused flash floods in Erbil city on 30th October and 17th December 2021. The rainfall intensities were 52 mm/day and 60 mm/day, respectively, causing destructive floods. The most affected areas were Zirin (north of Erbil) and Dara Too (northeast of Erbil), respectively, with very heavy property damages and tens of fatalities. The main reasons for the floods were the partially constructed embankments of the 150 m ring-road, and the urban development within large and wide drainage basins. Different climate data were used for the purpose of this article, with field checks. Godkänd;2023;Nivå 0;2023-01-02 (johcin)

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. Validerad;2022;Nivå 2;2022-02-18 (sofila);Funder: University of Kurdistan, Iran (grant no. 11-99-4469)

The main objective is to simulate the representation of rubber concrete with different percentages (0, 20, 40, 60, 80, 100%) of rubber using a finite element approach with ANSYS V.15 software to evaluate the behavior of concrete under the influence of mechanical force by using a standard cylinder to calculate its compressive strength, elasticity modulus and displacements for comparison with the previous experimental study, which showed a very close agreement of 5 to 12% when compared between them. The results showed that when the percentage of rubber increases, it leads to a decrease in the compressive strength, that is, its resistance, and an increase in the corresponding displacements and deformations that occur in them, as a result of the concentration of these stresses in the rubber limits. The results also showed that the meshing distribution of the analyzed elements by the ANSYS gave higher values of compressive strength and a decrease in the corresponding displacements when they were regular i.e. homogeneous compared to the irregular ones. Finally, through statistical analysis, it was found that the mean and arithmetic mean were acceptable and close when comparing the theoretical and practical results. Validerad;2022;Nivå 2;2022-02-21 (joosat)

Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models. Validerad;2022;Nivå 2;2022-06-27 (joosat)