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Abstract This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models’ accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India.

Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.

Abstract Groundwater resources are essential for drinking water, irrigation, and the economy mainly in semiarid environments where rainfall is limited. Currently, unpredictable rainfall due to climate change and pollution on the Earth’s surface directly affects groundwater resources. In this area, most people depend on groundwater resources for irrigation and drinking purposes, and every summer, most of the area depends on groundwater in a semiarid environment. Hence, we selected two popular methods, the analytical hierarchy process (AHP) and multiple influence factor (MIF) methods, which can be applied to map groundwater potential zones. Nine thematic layers, such as land use and land cover (LULC), geomorphology, soil, drainage density, slope, lineament density, elevation, groundwater level, and geology maps, were selected for this study using remote sensing and geographic information system (GIS) techniques. These layers are integrated in ArcGIS 10.5 software with the help of the AHP and MIF methods. The map of the groundwater potential zones in the study area revealed four classes, i.e., poor, moderate, good, and very good, based on the AHP and MF methods. The groundwater potential zone area is 241.50 (ha) Poor, 285.64 (ha) moderate, 408.31 (ha) good, and 92.75 (ha) very good using the AHP method. Similarly, the MIF method revealed that the groundwater potential classes were divided into four classes: 351.29 (ha) poor, 511.18 (ha), moderate, 123.95 (ha) good, and 41.78 (ha) very good. The results were compared to determine which methods are best for planning water and land resource development in specific areas that have basaltic rock and drought conditions. Both groundwater potential zone maps were validated with water yield data. The receiver operating characteristic (ROC) curve and area under the curve (AUC) model results are found to be 0.80 (good) and 0.93 (excellent) using the MIF and AHP methods, respectively; hence, the AHP method is best for delineation of groundwater potential zone maps and groundwater resource planning. The present study’s framework and the results will be valuable for improving the efficiency of irrigation, conserving rainwater and maintaining the ecosystem in India.

The Water Editorial Office retracts the article “Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective” [...]

To date, research on microbial fuel cells (MFCs) has. focused on the production of cost-effective, high-performance electrodes and catalysts. The present study focuses on the synthesis of silver nanoparticles (AgNPs) by Pseudomonas sp. and evaluates their role as an oxygen reduction reaction (ORR) catalyst in an MFC. Biogenic AgNPs were synthesized from Pseudomonas aeruginosa via facile hydrothermal synthesis. The physiochemical characterization of the biogenic AgNPs was conducted via scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV-visible spectrum analysis. SEM micrographs showed a spherical cluster of AgNPs of 20–100 nm in size. The oxygen reduction reaction (ORR) ability of the biogenic AgNPs was studied using cyclic voltammetry (CV). The oxygen reduction peaks were observed at 0.43 V, 0.42 V, 0.410 V, and 0.39 V. Different concentrations of biogenic AgNPs (0.25–1.0 mg/cm2) were used as ORR catalysts at the cathode in the MFC. A steady increase in the power production was observed with increasing concentrations of biogenic AgNPs. Biogenic AgNPs loaded with 1.0 mg/cm2 exhibited the highest power density (PDmax) of 4.70 W/m3, which was approximately 26.30% higher than the PDmax of the sample loaded with 0.25 mg/cm2. The highest COD removal and Coulombic efficiency (CE) were also observed in biogenic AgNPs loaded with 1.0 mg/cm2 (83.8% and 11.7%, respectively). However, the opposite trend was observed in the internal resistance of the MFC. The lowest internal resistance was observed in a 1.0 mg/cm2 loading (87 Ω), which is attributed to the high oxygen reduction kinetics at the surface of the cathode by the biogenic AgNPs. The results of this study conclude that biogenic AgNPs are a cost-effective, high-performance ORR catalyst in MFCs.

Landslide susceptibility mapping (LSM) is essential for land-use planning, as it helps to identify areas at risk of landslides and enables effective prevention measures to be taken. Various statistical and machine learning (ML) models are used in LSM, including SVM and ANFIS, which have shown promising results. However, determining which model performs better remains a key challenge. To address this issue, this paper aims to compare six hybrid models constructed with two well-known and powerful ML models, namely SVM and ANFIS, and three meta-heuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Cultural Algorithm (CA), for LSM in a case study in western Serbia. In the process of building the models, 359 landslide sites and 14 determinants were used. The accuracy of the models was evaluated using several indexes, including Root Mean-Squared Error (RMSE), coefficient of determination (R2), and Area under the Receiver Operating Characteristic Curve (AUROC). The modeling results showed that the SVM-GA model has the highest accuracy (AUROC = 0.78) in predicting landslide incidence, followed by the ANFIS-GA (AUROC = 0.775), SVM-CA (AUROC = 0.773), ANFIS-DE (AUROC = 0.771), SVM-DE (AUROC = 0.76), and ANFIS-CA (AUROC = 0.65) models in validation phase. Therefore, the study suggested that SVM-based hybrid models are more accurate than ANFIS-based models for LSM, and thus, modelers may use SVM-based hybrid models for such applications. This study provides valuable insights into identifying the most appropriate and effective models for LSM, which can help to mitigate the risks associated with landslides and ensure sustainable land-use practices.

Abstract Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were $${X}_{t-1},\; {X}_{t-2}, \;{X}_{t-3}, \;{X}_{t-4}, \; {X}_{t-12}$$ X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE $$\approx$$ ≈ 0.08 m), mean absolute error (MAE $$\approx$$ ≈ 0.06 m), and coefficient of determination (R 2 $$\approx$$ ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Groundwater is an important source of freshwater. At the same time, anthropogenic activities, in particular, industrialization, urbanization, population growth, and excessive application of fertilizers, are some of the major reasons for groundwater quality deterioration. Therefore, the present study is conducted to evaluate groundwater quality by using integrated water quality indices and a geospatial approach to identify the different water quality zones and propose management strategies for the improvement of groundwater quality. Groundwater quality was evaluated through the physicochemical parameters (pH, chloride (Cl−), fluoride(F−), iron (Fe−2), nitrate (NO3−1), nitrite (NO2), arsenic (As), total hardness, bicarbonate (HCO3−), calcium (Ca+2), magnesium (Mg+2), color, taste, turbidity, total dissolved solids (TDS)) and microbiological parameters including total coliforms, fecal coliforms, and Escherichia coli of samples collected from the water and sanitation agency (WASA) and urban units. Irrigation parameters crucial to the assessment, including (electrical conductivity (EC), residual sodium carbonates (RSC), and sodium adsorption ratio (SAR)), were also collected at more than 1100 sites within the study area of upper and central Punjab. After collecting the data of physicochemical parameters, the analysis of data was initiated to compute the water quality index for groundwater quality, a four-step protocol in which the Analytical Hierarchy Process (AHP) was used to determine the weights of selected parameters by generating a pairwise matrix, on the relative importance of parameters using the Satty scale. The index was then classified into five classes for quality assessment of drinking water (excellent, good, medium, bad, and very bad) and four classes for irrigation water quality assessment (excellent, good, permissible, and unsuitable). After computing the index values for drinking as well as irrigation purposes, the values were interpolated, and various maps were developed to identify the status of groundwater quality in different zones of the study area. Mitigation strategies for water pollution involve source control, such as monitoring industrial discharge points and managing waste properly. Additionally, treating wastewater through primary, secondary, or tertiary stages significantly improves water quality, reducing contaminants like heavy metals, microbiological agents, and chemical ions, safeguarding water resources. The findings highlight significant regional variations in water quality issues, with heavy metal concerns concentrated notably in Lahore and widespread emerging microbiological contamination across all studied divisions. This suggests a systemic problem linked to untreated industrial effluents and poorly managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions indicate water quality ranging from marginal to unfit, underscoring the urgency for remediation. Conversely, other divisions fall within a medium class, potentially suitable for drinking purposes. Notably, microbiological contamination at 27% poses a major challenge for water supply agencies, emphasizing the critical need for pre-disposal primary, secondary, and tertiary treatments. These treatments could potentially rehabilitate 9%, 35%, and 41% of the study area, respectively, pointing toward tangible, scalable solutions critical for safeguarding broader water resources and public health. With the current pace of water quality deterioration, access to drinking water is a major problem for the public. The government should prioritize implementing strict monitoring mechanisms for industrial effluent discharge, emphasizing proper waste management to curb groundwater contamination. Establishing comprehensive pre-disposal treatments, especially primary, secondary, and tertiary stages, is imperative to address the prevalent heavy metal and microbiological issues, potentially rehabilitating up to 41% of affected areas. Additionally, creating proactive policies and allocating resources for sustainable groundwater management are crucial steps for ensuring broader water resource security and public health in the face of deteriorating water quality. Therefore, urgent regional action is needed to address escalating anthropogenic threats to groundwater, emphasizing the crucial need for proactive measures to safeguard public health and ensure sustainable water resources.

Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling.

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections.

Accurate and reliable wave forecasting is crucial for optimizing the performance of various marine operations, such as offshore energy production, shipping, and fishing. Meanwhile, predicting wave height and wave energy is crucial for achieving sustainability as a renewable energy source, as it enables the harnessing of the power of wave energy efficiently based on the water-energy nexus. Advanced wave forecasting models, such as machine learning models and the semi-analytical approach, have been developed to provide more accurate predictions of ocean waves. In this study, the Sverdrup Munk Bretschneider (SMB) semi-analytical approach, Emotional Artificial Neural Network (EANN) approach, and Wavelet Artificial Neural Network (WANN) approach will be used to estimate ocean wave parameters in the Gulf of Mexico and Aleutian Basin. The accuracy and reliability of these approaches will be evaluated, and the spatial and temporal variability of the wave field will be investigated. The available wave characteristics are used to generate hourly, 12-hourly, and daily datasets. The WANN and SMB model shows good performance in the daily prediction of the significant wave height in both case studies. In the SMB model, specifically on a daily time scale, the Nash–Sutcliffe Efficiency (NSE) and the peak deviation coefficient (DCpeak) were determined to be 0.62 and 0.54 for the Aleutian buoy and 0.64 and 0.55 for the Gulf of Mexico buoy, respectively, for significant wave height. In the context of the WANN model and in the testing phase at the daily time scale, the NSE and DCpeak indices exhibit values of 0.85 and 0.61 for the Aleutian buoy and 0.72 and 0.61 for the Gulf of Mexico buoy, respectively, while the EANN model is a strong tool in hourly wave height prediction (Aleutian buoy (NSEEANN = 0.60 and DCpeakEANN = 0.88), Gulf of Mexico buoy (NSEEANN = 0.80 and DCpeakEANN = 0.82)). In addition, the findings pertaining to the energy spectrum density demonstrate that the EANN model exhibits superior performance in comparison to the WANN and SMB models, particularly with regard to accurately estimating the peak of the spectrum (Aleutian buoy (DCpeakEANN= 0.41), Gulf of Mexico buoy (DCpeakEANN = 0.59)).

This study aims to produce accurate geospatial 3D data from unmanned aerial vehicle (UAV) images. An image of a ≈ 0.05 km2 area of the Najran University campus in Najran, Saudi Arabia, was captured using a DJI Mavic Pro Platinum drone. Agisoft Metashape and Pix4dmapper programs were used to generate the solution. The horizontal and vertical accuracies of the obtained UAV solution were computed by comparing the coordinates of 21 ground control points (GCPs) with coordinates measured using the RTK GPS method. The accuracy of the four different GCP configurations was evaluated using both software packages. The root mean square error (RMSE) was calculated for some checkpoints. The generated model achieved the 2015 ASPRS accuracy standards for digital geospatial data, while horizontal RMSE values were 4–6 cm and vertical accuracy was 5–6 cm. The horizontal and vertical RMSE values were twice and three times the GSD, respectively.

Many practical issues arise when dealing with solid waste management, and there are also environmental effects to be considered. Selecting landfill sites requires extra care with respect to many factors such as the environment, health hazards for people, and the cost of transportation. Furthermore, cities have their own rules, methods, and practices for managing and selecting the best locations for collecting solid waste. In this research, multi-criteria decision analysis (MCDA) was presented and used to evaluate the appropriateness of and suggest the best locations for landfill sites in Najran, KSA. Some remote sensing data and the ArcGIS software were used to prepare nine thematic layers, including drainage density, groundwater depth, land use, soil type, road network, surface elevation, surface slope, distance from residential areas, and distance from protected areas. To evaluate the layer weightings, a questionnaire with pairwise comparisons was distributed among experts and analyzed using the analytical hierarchy process (AHP) and fuzzy set technique. The one-factor-at-a-time (OFAT) sensitivity test was conducted to test the sensitivity of the evaluated weightings. A landfill suitability index (LSI) map was created using raster calculator tools and divided into five classes: limited suitability (LSI value 1.39–2.49), least suitable (2.50–3.03), relatively suitable (3.04–3.48), suitable (3.49–3.91), and most suitable (3.92–4.66). According to the statistical analysis, 18.0% and 18.2% of the total area were within the most suitable and suitable landfill regions, while 21.2%, 14.9%, and 9.5% accounted for relatively suitable, least suitable, and limited suitability areas, respectively. The employed technique and its findings can provide an appropriate guideline to assist the municipality of Najran city, regional planners, and decision-makers in selecting an optimal landfill site in the future. This study also presented some recommendations to enhance the suitability map of landfill sites in Najran city.

The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process.

The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps.

The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC = 0.996).

Land use and climate change always induce significant changes in various parameters of the hydrologic cycle (e.g., surface runoff, infiltration, evapotranspiration). The Wadi El-Assiuti downstream area in the Eastern Desert of Egypt is one of the most promising areas for development that is suffering from insufficient water availability and inadequate water quality for different purposes. The main goal of this research is to evaluate the changes in groundwater quality, land use, and climate in association with geology and flooding during three periods within the years 1997–2019 in the downstream portion of Wadi El-Assiuti in the Eastern Desert of Egypt, using spatiotemporal variation associated with groundwater hydrochemical analysis and GIS techniques. About 133 groundwater samples were collected to examine groundwater quality changes over time. Different groundwater quality indices were calculated, and the results show that TDS levels of groundwater in the study area ranged between 1080–2780 mg/L, 672–4564 mg/L, and 811–6084 mg/L, while SAR levels varied within 6.15–15.34, 1.83–28.87, and 1.43–30.57 for the years 1997, 2007, and 2019, respectively. Both RSBC and SSP values exhibited significantly increasing trends over time. KR values were within 1.36–4.06 in 1997, 0.58–14.09 in 2007, and 0.35–14.92 in 2019; MAR values were within 6.9–45.2 in 1997, 20.79–71.5 in 2007, and 17.71–75.81 in 2019; and PI values were within 60.16–83 in 1997, 45.56–101.03 in 2007, and 42.51–148.88 in 2019. Across the entire study area, ongoing land use changes increased from 1.1% in 1997 to 4.1% in 2019. Findings pointed to the significant contribution of the deep Nubian Sandstone Aquifer to the groundwater aquifer at Wadi El-Assiuti through fractures and deep faults. Given the climatic conditions from 1997–2019, these changes may have affected water quality in shallow aquifers, especially with increasing evaporation. Realizing the spatiotemporal variation of the aquifer recharge system, land use development, and climate change clearly would help in water resource management. This study revealed that flooding events, deep-seated geologic structures, and land use development associated with human activities have the highest impact on groundwater quality.

This study focuses on highlighting the major effects on the agricultural activities caused by the reduction in water resources in Najran, Saudi Arabia. Since the last decade, there has been a sharp decrease in the availability of water in the area as reported by many researchers due to urban sprawl, excessive groundwater extraction causing a decrease in land fertility, and destruction of soil structure. The water resources of the area and the agricultural heritage is endangered, and also the environmental, economic, and social impact of these actions can be severe if such activities continue in the future. The purpose of the study is to obtain detailed information regarding the major causes of water depletion in the Najran Valley as per the opinion of the residents. A survey questionnaire was designed with a high level of flexibility, covering the key issues including causes of groundwater depletion, factors that affected agricultural activities and irrigation practices and the environmental impact of the Najran Dam, and the risks of floods related to it. An overwhelming response was received from the people of the area. Significant results were obtained from the analysis, and the major effects and causes were highlighted as conclusions. The study shall help the city planners and policymakers in identifying the major causes of water depletion and the actual needs of the residents in order to manage the water resources of the area efficiently. Understanding the public opinion on the issues discussed in this study shall provide a base to develop management options for water resources problems suited to local contexts that fits the need of the community.

FAO Blaney-Criddle has been generally an accepted method for estimating reference crop evapotranspiration. In this regard, it is inevitable to estimate the b-factor provided by the Food and Agriculture Organization (FAO) of the United Nations Irrigation and Drainage Paper number 24. In this study, five soft computing methods, namely random forest (RF), M5 model tree (M5), support vector regression with the polynomial function (SVR-poly), support vector regression with radial basis function kernel (SVR-rbf), and random tree (RT), were adapted to estimate the b-factor. And Their performances were also compared. The suitable hyper-parameters for each soft computing method were investigated. Five statistical indices were deployed to evaluate their performance, i.e., the coefficient of determination (r2), the mean absolute relative error (MARE), the maximum absolute relative error (MXARE), the standard deviation of the absolute relative error (DEV), and the number of samples with an error greater than 2% (NE > 2%). Findings reveal that SVR-rbf gave the highest performance among five soft computing models, followed by the M5, RF, SVR-poly, and RT. The M5 also derived a new explicit equation for b estimation. SVR-rbf provided a bit lower efficacy than the radial basis function network but outperformed the regression equations. Models’ Applicability for estimating monthly reference evapotranspiration (ETo) was demonstrated.

Abstract Digital surface models, land use and rainfall data were used to simulate water areas using Hydrologic Engineering Centres River Analysis System (HEC‐RAS) software. Multi‐temporal synthetic aperture radar (SAR) was used for the detection of flood prone area to calibrate HEC‐RAS, due to the lack of data validation in the New Cairo City, Egypt. The thresholding water detection method was applied to two Sentinel‐1 images, one pre‐ and one post‐flash flood event from April 24 to 27, 2018. The threshold method was used to detect water areas from SAR Sentinel‐1 images. Feature statistical agreement F1 and F2 values ranged from 73.4 to 77.7% between water areas extracted based on backscattering values between 19.97 and 16.53 in decibels (dB) and reference water areas obtained using an optical image of the Sentinel‐2 satellite. The similarity between simulated HEC‐RAS two‐dimensional (2D) of water areas and reference water areas based on SAR data ranged between 74.2 and 89.7% using feature statistical agreement values F1 and F2. It provides a clear suggestion that, in the absence of field observations, SAR data can be used to calibrate the model. Two flood hazard maps created based on water velocity and depth were obtained from HEC‐RAS 2D simulation. The obtained maps indicated that 11% of the roads and 50% of the buildings in New Cairo City are exposed to high hazard areas. Furthermore, 28% of the bare land is situated in a very high vulnerability area. We recommend the use of obtained hazard map in supporting emergency response, and designing effective emergency plans.

Digital Elevation Model is imperative to many earth surface process analyses. In this study, the quality of DEMs acquired by SRTM ver.3 and ASTER ver.2 is evaluated. The reference levels produced from GPS elevations, and the topographic map is used to assess the vertical accuracy of SRTM and ASTAR DEMs in Najran city, Saudi Arabia. The GPS reference elevations gave us the values of ±5.94 m and ±5.07 m for used SRTM and ASTER DEMs. Also, by using elevation from the topographic map as a reference elevations the obtained accuracy was ±6.87 m and ±7.97 m for SRTM and ASTER DEMs. For our study area, the 30 m SRTM elevations data featured a much greater absolute vertical accuracy than the absolute vertical accuracy value of ±16 m, which published in the SRTM data specification. Keywords: DEM, Accuracy assessment, SRTM, ASTER, Topographic maps, GPS

In this article, the local spatial correlation of multiple remote sensing datasets, such as those from Sentinel-1, Sentinel-2, and digital surface models (DSMs), are linked to machine learning (ML) regression algorithms for flash floodwater depth retrieval. Edge detection filters are applied to remote sensing images to extract features that are used as independent features by ML algorithms to estimate flood depths. Data of dependent variables were obtained from the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 2D) simulation model, as applied to the New Cairo, Egypt, post-flash flood event from 24–26 April 2018. Gradient boosting regression (GBR), random forest regression (RFR), linear regression (LR), extreme gradient boosting regression (XGBR), multilayer perceptron neural network regression (MLPR), k-nearest neighbors regression (KNR), and support vector regression (SVR) were used to estimate floodwater depths; their outputs were compared and evaluated for accuracy using the root-mean-square error (RMSE). The RMSE accuracy for all ML algorithms was 0.18–0.22 m for depths less than 1 m (96% of all test data), indicating that ML models are relatively portable and capable of computing floodwater depths using remote sensing data as an input.

Flash flood in the cities led to high levels of water in the streets and roads, causing many problems such as bridge collapse, building damage and traffic problems. It is impossible to avoid risks of floods or prevent their occurrence, however it is plausible to work on the reduction of their effects and to reduce the losses which they may cause. Flash flood mapping to identify sites in high risk flood zones is one of the powerful tools for this purpose. Mapping flash flood will be beneficial to urban and infrastructure planners, risk managers and disaster response or emergency services during extreme and intense rainfall events. The objective of this paper is to generate flash flood map for Najran city, Saudi Arabia, using satellite images and GIS tools. To do so, we use SPOT and SRTM DEMs data for which accuracy assessment is achieved by using check points, obtained by GPS observations. Analytical Hierarchical Process (AHP) is used to determine relative impact weight of flood causative factors to get a composite flood hazard index (FHI). The causative factors in this study are runoff, soil type, surface slope, surface roughness, drainage density, distance to main channel and land use. All used data are finally integrated in an ArcMap to prepare a final flood hazard map for study area. The areas in high risk flood zones are obtained by overlaying the flood hazard index map with the zone boundaries layer. The affected population number and land area are determined and compared.