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Abstract This study presents an innovative approach for predicting water and groundwater quality indices (WQI and GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges of scarcity and pollution in arid regions. Recent literature highlights the increasing attention towards WQI based on water pollution index (WPI) and GWQI as essential tools for simplifying complex hydrogeological data, thereby facilitating effective groundwater management and protection. Unlike previous works, the present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) algorithms. This approach marks the first application of a non-parametric kernel for groundwater quality pollution index prediction in Saudi Arabia, offering a significant advancement in the field. Through laboratory analysis and the combination of various machine learning (ML) techniques, this study enhances prediction capabilities, particularly for unmonitored sites in arid and semi-arid regions. The study’s objectives include feature engineering based on dependency sensitivity analysis to identify the most influential variables affecting WQI and GWQI, and the development of predictive models using ANFIS, GPR, and DT for both indices. Furthermore, it aims to assess the impact of different data portions on WQI and GWQI predictions, exploring data divisions such as (70% / 30%), (60% / 40%), and (80% / 20%) for training and testing phase, respectively. By filling a critical gap in water resource management, this research offers significant implications for the prediction of water quality in regions facing similar environmental challenges. Through its innovative methodology and comprehensive analysis, this study contributes to the broader effort of managing and protecting water resources in arid and semi-arid areas. The result proved that GPR-M1 exhibited exceptional testing phase accuracy with RMSE = 0.0169 for GWQI. Similarly, for WPI, the ANFIS-M1 achieved high testing predictive skills with RMSE = 0.0401. The results emphasize the critical role of data quality and quantity in training for enhancing model robustness and prediction precision in water quality assessment.

Abstract The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

The occurrence of dam failures and the consequent floods pose substantial risks to submerged regions, and their unpredictability increases the hazard of their impact. This research simulates the flood event scenario due to the dam failure of the Hemrem dam in Diyala Governorate, Iraq. HEC-RAC model jas been used to modeling the Hemren dam failure. The generated flood waves are routed downstream until the confluence of the Diyala and Tigris rivers, covering a large study area of 5,378.5 km². The configuration of the model's geometry is formulated using the Digital Elevation Model (DEM) that encompasses the geographical extent of the Diyala River study area, from Hemren Dam to the confluence with the Tigris River south of Baghdad. The length of this reach is approximately 210 km. Based on the inundation maps, the region's geography is classified as having catastrophic constraints due to the water depth and flow rate. The results demonstrate how to predict the magnitude of floods and highlight the severity if Hemren Dam were to fail. This underscores the need for effective risk management. Furthermore, the maps created by this study can be utilized to develop long-term flood control plans.

The hydraulic jump occurs in open channels and downstream of hydraulic structures because these features change the flow regime from super-critical to sub-critical. To prevent downstream channel erosion and guarantee the construction of a hydraulic jump, a stilling basin is utilized. The effect of sand-roughened beds on hydraulic jump characteristics was investigated using an experimental study. After that, new machine learning models including Emotional neural network (EANN), Hammerstein Weiner model (HWM), Elman neural networks ENN, and Extreme learning neural networks (ELNN) were adopted to estimate the ratio of hydraulic jump length to initial water depth. Different evaluation criteria such as determination Coefficient (R2), Mean Square Error (MSE), Correlation Coefficient (R), and Rout Mean Square Error (RMSE), scatter plots, time series comparison, and Spiral plots have been used to evaluate the models' performance. The results revealed that the Elman NN model outperformed the other models in estimating the ratio of hydraulic jump length to the initial depth (Lj/y1), while the HWM model has a weaker performance than other models in estimating Lj/y1.

Climate change resulted in dramatic change in the monsoon precipitation rates in Malaysia, contributing to repetitive flooding events. This research examines different substantial practicalities of machine learning (ML) in performing high-performance and accurate FF. The case study was The Dungun River. IGISMAPs datasets of water level and rainfall were investigated (1986–2000). The Forecasting was implemented for current (1986–2000) and near future (2020–2030). ML algorithms were Logistic Regression, K-Nearest neighbors, Support Vector Classifier, Naive Bayes, Decision tree, Random Forest, and Artificial Neural Network. Simulations were run in the Colab software tool. The results revealed that between 1986 and 2000, there would be an average of (18–55) floods around the Dungun River Basin. Floods occurred rarely before 1985. They have been common since 2000. 35 floods occurred annually on average since 2000. It is predicted that between 2020 and 2030, flooding events would grow on the Dungun River Basin. Most floods occurred due to rainfall between 1 and 500 mm. The maximum frequency of flooding was measured at 110 occurrences at a rainfall of 250 mm. The overall accuracies were 75.61%/ random forest, 73.17%/ KNN, and logistic regression/ 48.78%. Overall, the ANN models had a competitive mean accuracy of 90.85%. HIGHLIGHTS Comprehensive analysis of the events has been carried out.; Different machine learning models capture the events of floods in the main river in Malaysia.;

Abstract Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R 2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Realizing the intricate relationships between drought, vegetation dynamics, and climate change is essential for sustainable resource management. Although temperature and rainfall patterns are the primary determinants of these fluctuations, human activity also plays a significant role. Recent decades have witnessed significant climate change events, particularly in peninsular India. Analyzing these year-by-year variations in rainfall and temperature is essential for informed decision-making. This knowledge can guide the development of innovative adaptation strategies to ensure sustainable livelihoods in the region. This study utilizes the Google Earth Engine platform to analyze yearly climate data and relevant geographical indices from 2003 to 2023 across Peninsular India. The analysis reveals a statistically significant increase in mean annual rainfall (0.262 mm/year) alongside a slight rise in regional land surface temperature (LST) trends (0.102 °C/year). However, yearly average anomaly values for LST also show an upward trend, rising from 2.56 in 2003 to 3.23 in 2023. This suggests a potential shift in rainfall patterns, with potential consequences for water availability. Rising temperatures coupled with altered rainfall patterns can lead to water scarcity, especially in regions reliant on rain-fed agriculture. This has a direct impact on crop yields and overall agricultural productivity. Despite rising temperatures, the analysis using drought indices suggests a decline in average annual drought severity across Peninsular India, with values decreasing from 0.33 in 2003 to 2.70 in 2023. Interestingly, we found a strong positive association between rainfall and vegetation indices, while rainfall and LST exhibited a negative correlation. Interestingly, while rainfall and LST exhibited a negative correlation, a strong positive association was found between rainfall and vegetation indices. These comprehensive findings hold significant potential for informing future climate projections and promoting sustainable development in peninsular India through evidence-based applications.

This study aims to identify the influential parameters and heavy metals in water and assess the water quality classification at the Alpine glacial lakes and rivers in three districts of Pakistan. For this purpose, nine water quality parameters (Cd, Cr, Pb, Ni, Fe, As, and TDS) in mg/L, pH, Ec µS/Cm are used to compute the Water Quality Index (WQI). The Boruta approach was utilized for the identification of influential parameters associated with the water quality classes. Moreover, we employed supervised machine learning models, including a decision tree, the k-nearest neighbor method, a neural network model (multi-layer perception), a support vector machine, and a random forest, to predict and validate the water quality class. The performance of all algorithms is assessed by an accuracy metric. The accuracy rates for the validation set were observed to be 83% for the decision tree model, 75% for the K-nearest neighbor method, 83% for the neural network, 88% for the support vector machine, and 88% for the random forest model. Water quality assessments for observed locations specify significant insights, revealing that 49% of the locations exhibit low water quality. According to the current study, the government should address problems with water quality in Pakistan’s impacted areas by implementing suitable measures designed water monitoring systems and innovative technologies.

Abstract One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950–2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.

In this study, three machine learning models, namely, the Multilayer Perceptron Neural Networks (MLPNN), the Generalized Regression Neural Networks (GRNN) and the Radial Basis Function Neural Networks (RBFNN) were used for predicting seepage flow through an earthfill dam. Moreover, obtained results were compared with those obtained from the standard Multiple Linear Regression (MLR). The three models were developed using piezometer elevations observed at seven different piezometers, in addition to the related reservoir water level and the periodicity for a period of seven years. Obtained results indicated that the GRNN model had substantially better prediction performance than the RBFNN, MLPNN, and the standard MLR models with statistical values of coefficient of correlation R = 0.981, root mean square error RMSE = 0.386 L/s, and a mean absolute error MAE = 0.95 L/s. Moreover, including the periodicity factors improves prediction accuracy of the machine learning models.

Abstract The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject of extensive research. However, a comprehensive understanding of the thermodynamic aspects of dye solution adsorption is lacking. Previous studies have primarily focused on enhancing the adsorption capacity of methylene blue dye. This study aimed to develop an environmentally friendly and cost-effective method for treating methylene blue dye wastewater and to gain insights into the thermodynamics and kinetics of the adsorption process for optimization. An adsorbent with selective methylene blue dye adsorption capabilities was synthesized using rice straw as the precursor. Experimental studies were conducted to investigate the adsorption isotherms and models under various process conditions, aiming to bridge gaps in previous research and enhance the understanding of adsorption mechanisms. Several adsorption isotherm models, including Langmuir, Temkin, Freundlich, and Langmuir–Freundlich, were applied to theoretically describe the adsorption mechanism. Equilibrium thermodynamic results demonstrated that the calculated equilibrium adsorption capacity (q e ) aligned well with the experimentally obtained data. These findings of the study provide valuable insights into the thermodynamics and kinetics of methylene blue dye adsorption, with potential applications beyond this specific dye type. The utilization of rice straw as an adsorbent material presents a novel and cost-effective approach for MB dye removal from wastewater.

Abstract Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models’ performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model.

Abstract Perfluorooctane sulfonic acid (PFOS), a perfluoroalkyl substance, has engendered alarm over its presence in water sources due to its intrinsic toxicity. Hence, there is a pressing need to identify efficacious adsorbents capable of removing PFAS derivatives from water. To achieve this, batch adsorption studies under various circumstances were employed to tune amorphous polymer networks regarding their morphological configuration, heat durability, surface area and capacity to adsorb PFOS in water. A facile, one-pot nucleophilic substitution reaction was employed to synthesize amorphous polymer networks using triazine derivatives as building units for monomers. Notably, POP-3 exhibited a superlative adsorption capacity, with a removal efficiency of 97.8%, compared to 90.3% for POP-7. POP-7 exhibited a higher specific surface area (SBET) of 232 m2 g−1 compared to POP-3 with a surface area of 5.2 m2 g−1. Additionally, the study emphasizes the importance of electrostatic forces in PFOS adsorption, with pH being a significant element, as seen by changes in the PFOS sorption process by both polymeric networks under neutral, basic and acidic environments. The optimal pH value for the PFOS removal process using both polymers was found to be 4. Also, POP-7 exhibited a better thermal stability performance (300 °C) compared to POP-3 (190 °C). Finally, these findings indicate the ease with which amorphous polymeric frameworks may be synthesized as robust and effective adsorbents for the elimination of PFOS from waterbodies.

In this study, a novel method for the high-performance treatment of oily wastewater was introduced using a tungsten (VI) oxide (WO3)/MXene composite membrane based on poly (arylene ether sulfone) (PAES). Composite membranes were fabricated with superhydrophilic (SH) and superoleophobic (SO) characteristics, which allow for the high-performance treatment of oily wastewater. The fabricated composite membrane can also photodegrade organic types of pollutants with just a short period of UV, enabling self-cleaning and anti-fouling properties. Moreover, the comprehensive characterization of the composite membrane through FTIR, SEM, and XRD analyses yielded valuable insights. The FTIR analysis revealed the characteristic peaks of WO3, MXene, PAES, and the synthesized composite membrane, providing essential information on the chemical composition and properties of the materials. The XRD results demonstrated the crystal structures of WO3, MXene, PAES, and the synthesized composite membrane, further enhancing our understanding of the composite membrane. Additionally, the SEM images illustrated the surface and cross-section of the fabricated membranes, highlighting the differences in pore size and porosity between the PAES membrane and the WO3–MXene composite membrane, which directly impact permeate flux. The study showed that the composite membrane had a remarkable recovery time of only 0.25 h, and the efficiency of the separation process and water flux recovered to 99.98% and 6.4 L/m2.h, respectively. The joint influence of WO3 and MXene on composite membranes degraded contaminants into non-polluting substances after sunlight irradiation. This process effectively solves the treatment performance and decrease in permeate flux caused by contamination. The technology is membrane-based filtration, which is a simple and advanced method for treating polluted water. This innovative work offers promising solutions to address water pollution challenges and holds potential for practical applications from a self-cleaning and anti-fouling point of view.

Abstract Membrane filtration exhibit operational limitations such as biofouling, which leads to concentration polarization and reduces permeability and selectivity, despite advantages such as low operating cost, high selectivity, and permeability. In recent years, the antibacterial properties of silver nanoparticles (AgNPs) have been investigated for improving membrane processes; however, the fouling phenomena in presence of AgNPs in the membrane matrix have not been fully discussed. Herein, the antifouling properties of a poly (acrylonitrile-styrene) copolymer incorporated with AgNPs were studied in a microfiltration membrane process. The Creighton method was used to synthesize AgNPs, and the effects of AgNPs on the porosity, morphology, pore size, mechanical strength, permeability, and selectivity of the membranes were investigated. Moreover, to investigate the biofouling of the obtained membranes, microfiltration of industrial oily wastewater was performed at constant pressure over three cycles. Using AgNPs in the membrane matrix resulted in enhanced antifouling properties of the copolymer membrane, which is related to the structure of the AgNPs in the casting solution, as proven by SAXS analysis. The results show that the CFU% for Staphylococcus aureus and E.coli reach 2% and 6%, respectively. Finally, the Derjaguin–Landau–Verwey–Overbeek (DLVO) thermodynamic model was applied to study the antifouling mechanism, correctly predict the separation behavior in the membrane, and design, simulate, and optimize the separation processes in the membrane separation plantsa. The DLVO model could predict the separation behavior in the synthesized membranes, and the poly(acrylonitrile-styrene) copolymer membranes containing AgNPs were proven have promising industrial wastewater treatment applications.

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

AbstractThis article addresses the class imbalance problem in urban gain modelling (UGM) of Tabriz and Isfahan megacities in Iran by proposing novel cost-sensitive machine learning models, namely cost-sensitive support vector machine (CSVM), random forest (CRF) and artificial neural network (CANN). Random sampling, a frequently utilized method, fails to effectively tackle this issue by biasing models towards no change samples, which outnumber change samples. The results showed that CRF exhibited the highest accuracy (AUC = 0.560), followed by CANN (AUC = 0.557) and CSVM (AUC = 0.448) in Isfahan. In Tabriz, CRF (AUC = 0.809) and CANN (AUC = 0.818) excelled, outperforming balanced sampling models constructed with ANN, RF and SVM with the AUROC of ANN and RF boosted by 15% and 2% in validation. By emphasizing the significance of addressing class imbalance appropriately, this research highlights the improvement in modelling outcomes achievable through the cost-sensitive models especially in Tabriz case.

AbstractDrought monitoring is a complicated issue as it requires multiple meteorological variables to monitor and anticipate drought accurately. Therefore, developing a method that enables researchers, data scientists, and planners to comprehend drought mitigation policies more accurately is essential. In this research, based on the concepts behind the calculation of the Standardized Precipitation Evapotranspiration Index (SPEI), a new drought index is proposed for regional drought monitoring: the Modified Standardized Precipitation Evapotranspiration Index (MSPEI). The potential of the proposed index is based on the estimation of Reference Evapotranspiration (ETo). Therefore, the Modified Hargreaves-Samani (MHS) equation based on fuzzy logic calibration is used to estimate ETo. The proposed index is validated on ten meteorological stations in Pakistan at a one-month time scale. Afterward, based on the Pearson correlation, the performance of the proposed index is compared with the commonly used drought index (SPEI). Results showed a significant correlation (r > 0.7) between the quantitative values of MSPEI and SPEI for all ten stations. Moreover, a modified Tjostheims coefficient is used to estimate and test the spatial correlation between SPEI and MSPEI for different drought classes. According to our findings, the association between the SW, ND, ED, EW, MW, and SD patterns of MSPEI and SPI is 0.74, 0.834, 0.673, 0.592, 0.393, and 0.434, respectively. Meanwhile, considering the significance of future drought trend detection, this research is further extended to detect the future trend of MSPEI by using the Hurst index. In accordance with the results, Bahawalnagar, Sialkot, Lahore, Kotli, and Gilgit all have HI values greater than 0.5 (0.63, 0.58, 0.56, 0.55, and 0.53, respectively). In contrast, Muzaffarabad, Skardu, and Jhelum have HI values 0.47, 0.45 and 0.38, respectively; however, HI values of 0.5 are observed at Dera Ismail Khan (DIK) and Islamabad. Therefore, this research provides a basis for developing and enhancing drought hazard characterization, encouraging researchers and policymakers to monitor and forecast regional droughts using a more accurate drought index.

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

The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques.

Heavy metal pollution is a global problem that necessitates the development of innovative and environmentally friendly water treatment technologies. Polyoxazoline polymers, known for their biocompatibility, are explored for lead ion removal in water treatment. Poly 2-Methoxycarbonylpropyl-2-oxazoline is integrated into activated carbon via in situ polymer growth, optimizing loading through live polymerization. This study investigates intricate interactions between lead ions and functional groups, such as amide moieties and ester functionalities, in the resulting polyoxazoline-modified activated carbon composite (POZ-AC). This pioneering research opens avenues for the application of polyoxazoline polymers in water treatment, leveraging their established success in biomedical fields. The removal of lead ions by POZ-ACs followed the Langmuir isotherm and pseudo-second-order kinetic model. The results showed that POZ-AC-20 had excellent adsorption capacity of 365 mg/g, achieved in a relatively short time of 37 min. Furthermore, the adsorbent maintained its performance for seven cycles, demonstrating its high reusability potential. However, the adsorption performance of POZ-ACs after seven adsorption–desorption cycles was gradually decreased due to polymer release into the water media because of the high degree of solubility of polyoxazoline polymers in water. This study provides critical insight into the potential use of polyoxazoline polymers, demonstrating their superior potential in water treatment applications, particularly since it is the first time these polymers have been explored for this purpose. Future research should focus on developing polyoxazoline polymers with less solubility in water while maintaining a high removal performance.

The availability of water is crucial for the growth and sustainability of human development. The effective management of water resources is essential due to their renewable nature and their critical role in ensuring food security and water safety. In this study, the multi-step-ahead modeling approach of the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) was utilized to gain insights into and forecast the fluctuations in water resources within Saudi Arabia. This study was conducted using mascon solutions obtained from the University of Texas Center for Space Research (UT-CSR) over the period of 2007 to 2017. The data were used in the development of artificial intelligence models, namely, an Elman neural network (ENN), a backpropagation neural network (BPNN), and kernel support vector regression (k-SVR). These models were constructed using various input variables, such as t-12, t-24, t-36, t-48, and TWS, with the output variable being the focus. A simple and weighted average ensemble was introduced to improve the accuracy of marginal and weak predictive results. The performance of the models was assessed with the use of several evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (CC), and Nash–Sutcliffe efficiency (NSE). The results of the estimate indicate that k-SVR-M1 (NSE = 0.993, MAE = 0.0346) produced favorable outcomes, whereas ENN-M3 (NSE = 0.6586, MAE = 0.6895) emerged as the second most effective model. The combinations of all other models exhibited accuracies ranging from excellent to marginal, rendering them unreliable for decision-making purposes. Error ensemble methods improved the standalone model and proved merit. The results also serve as an important tool for monitoring changes in global water resources, aiding in drought management, and understanding the Earth’s water cycle.

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

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.

Different regression-based machine learning techniques, including support vector machine (SVM), random forest (RF), Bagged trees algorithm (BaT), and Boosting trees algorithm (BoT) were adopted for modeling daily reference evapotranspiration (ET0) in a semi-arid region (Hemren catchment basin in Iraq). An assessment of the methods with various input combinations of climatic parameters, including solar radiation (SR), wind speed (WS), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin), indicated that the RF method, especially with Tmax, Tmin, Tmean, and SR inputs, provided the best accuracy in estimating daily ET0 in all stations, while the SVM had the worst accuracy. This work will help water users, developers, and decision makers in water resource planning and management to achieve sustainability.

A wide range of studies has been carried out to describe the equilibrium data of adsorption for the surface adsorption process. However, no extensive investigation has been carried out to evaluate the oil sludge based activated carbon surface adsorption. Therefore, the possibility of carbon active production using different oil sludges and consequently the adsorption mechanism of these kind of adsorbents is still unknown. In this study, a novel low-cost approach was introduced to synthesize the activated carbon using oil sludge applying a two-step process including carbonization and chemical activation. In this way, four different types of oil sludges were characterized and then applied to synthesize different carbon actives and their performance were investigated as an adsorbent. The results showed that all synthesized activated carbons, with about 6% ash and pH = 7 and the specific surface area of 110 m2/gr, have the ability to treatment of oily wastewater; which can be referred to the high carbon content (>80%). The iodine number and the efficiency of prepared activated carbon were obtained as 406.8 mg/g and 94%, respectively. The adsorption process was also studied at different process conditions such as temperature (308–338 K), pH value (3–9) and adsorbent amount (50–200 mg/L) to find the optimum condition for wastewater treatment. The results show that the pH value has an optimum in the adsorption rate (the maximum adsorption was measured at pH = 5) and the adsorption capacity can be reduced by increasing the temperature or decreasing the adsorbent amount. Moreover, three different adsorption isotherm models were applied, i.e., Langmuir, Temkin, and Freundlich isotherms; which the Langmuir equation was more suitable than others investigated isotherm models with R2 ≈ 0.999.

The study of the infiltration process is considered essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of the subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of the infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: random tree, random forest (RF) and M5 tree, are employed to model the infiltration characteristics using basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, and the obtained results suggest that the performance of the RF model is better than the other applied models with coefficient of determination (R2) = 0.97 and 0.97, root mean square error (RMSE) = 8.10 and 6.96 and mean absolute error (MAE) = 5.74 and 4.44 for infiltration rate and cumulative infiltration respectively. The RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. The results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set. HIGHLIGHTS Infiltration rate and cumulative infiltration characteristics in semi-arid region were modelled using regression tree.; Random forest outperforms random tree as well as M5 tree to predict the infiltration characteristics.; Time is observed as the most important parameter in affecting the prediction performance of both the infiltration rate and the cumulative infiltration of soil.;

The shortage of water resources has caused extensive research to be conducted in this field to develop effective, rapid, and affordable wastewater treatment methods. For the treatment of wastewater, modern oxidation techniques are desirable due to their excellent performance and simplicity of implementation. In this project, wet impregnation and the hydrothermal technique were applied to synthesize a modified catalyst. Different analysis methods were used to determine its characteristics, including XRD, BET, FT-IR, NH3−TPD, and FE-SEM. The catalyst features a spherical shape, large surface area, high crystallinity, and uniform active phase dispersion. In order to eliminate the methylene blue dye as a modeling effluent, the catalyst’s performance was examined in a heterogeneous quasi-electro-Fenton (EF) reaction. The impact of various performance characteristics, such as catalyst concentration in the reaction medium, solution pH, and current intensity between the two electrodes, was elucidated. According to the results, the best operational circumstances included a pH level of 2, a catalyst concentration of 0.15 g/L, and a current of 150 mA, resulting in the greatest elimination efficiency of 101%. The catalyst’s performance was stable during three consecutive tests. A pseudo-first-order model for the elimination reaction’s kinetics was developed, which showed acceptable agreement with the experimental results. This study’s findings help clarify how well the heterogeneous zeolite catalyst functions in the pseudo-EF reaction. The results revealed the method’s potential to be implemented in wastewater treatment. An artificial neural network model is utilized to predict the removal percentage. The hyperparameter tuning is used to find the best model, and the model achieved an MAE of 1.26% and the R2 was 0.99.

Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC), turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. The violin plot of NF-GWO-predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.

Predicting reservoir water levels helps manage droughts and floods. Predicting reservoir water level is complex because it depends on factors such as climate parameters and human intervention. Therefore, predicting water level needs robust models. Our study introduces a new model for predicting reservoir water levels. An extreme learning machine, the multi-kernel least square support vector machine model (MKLSSVM), is developed to predict the water level of a reservoir in Malaysia. The study also introduces a novel optimization algorithm for selecting inputs. While the LSSVM model may not capture nonlinear components of the time series data, the extreme learning machine (ELM) model—MKLSSVM model can capture nonlinear and linear components of the time series data. A coati optimization algorithm is introduced to select input scenarios. The MKLSSVM model takes advantage of multiple kernel functions. The extreme learning machine model—multi-kernel least square support vector machine model also takes the benefit of both the ELM model and MKLSSVM model models to predict water levels. This paper’s novelty includes introducing a new method for selecting inputs and developing a new model for predicting water levels. For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. The testing means absolute of the same models was 0.710, 0.742, 0.832, 0.871, 0.912, and 0.919, respectively. The Nash–Sutcliff efficiency (NSE) testing of the same models was 0.97, 0.94, 0.90, 0.87, 0.83, and 0.18, respectively. The ELM-MKLSSVM model is a robust tool for predicting reservoir water levels.

Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980–2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.

Accurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%–50% (scenario-1), 60%–40% (scenario-2), and 70%–30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%–30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study region.

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.

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.

Gross primary production (GPP) is a key component in assessing the global change in carbon uptake and in evaluating the impacts of climate change on terrestrial ecosystems. A decrease in the photosynthetic rate due to stomata closing by vegetation could have an impact on GPP. Nonetheless, the uncertainty in long-term GPP patterns and their resilience to drought disturbances has not yet been examined thoroughly. In this study, four state-of-the-art GPP datasets, including the revised EC-LUE algorithm-driven GPP (GLASS-GPP), the EC flux tower upscaling-based GPP (FluxCom-GPP), the MODIS algorithm-driven GPP model (GIMMS-GPP), and the vegetation photosynthesis model-GPP (VPM-GPP), were used to assess GPP characteristics in the Middle East region for 36 years spanning the period of 1982 to 2016. All investigated datasets revealed an increasing trend over the study period, albeit with a more pronounced upward trend for the VPM-GPP dataset in the most recent decades (2000–2016). On the other hand, FluxCom-GPP exhibited less variability than the other datasets. In addition, while GLASS-GPP presented a significant increasing trend in some parts of the region, significant negative trends dominated the other parts. This study defined six significant drought episodes that occurred in the Middle East region between 1982 and 2017. The most severe drought events were recorded in 1985, 1989–1990, 1994, 1999–2001, 2008, and 2015, spreading over more than 15% of the total area of the region. The extreme droughts accounted for a high decline in GPP in the north of Iraq, the northeast of Syria, and the southwest of Iran, where 20.2 and 40.8% of the ecosystem’s GPP were severely non-resilient to drought according to the GLASS and VPM-based GPP responses, respectively. The spatial distribution patterns of the correlations between the SEDI and GPP products were somewhat similar and coherent. The highest positive correlations were detected in the central and western parts of Turkey, the western and northeastern parts of Iran, and north Iraq, which showed anomalous r values (r = 0.7), especially for the SEDI-VPM and SEDI-FluxCom GPP associations. The findings of this study can provide a solid base for identifying at-risk regions in the Middle East in terms of climate change impacts, which will allow for better management of ecosystems and proper implementation of climate policies.

In this research, the potential improvement of some geotechnical characteristics of soft clay soil using the low Calcium fly ash was evaluated. (These characteristics include unit weight, shear strength, compaction characteristics and soil plasticity characteristics). In addition, the X-ray diffraction test was performed to measure the mineralogical changes in the soft clay soil when the low Calcium fly ash is added. The ordinary Portland cement was used to activate the fly ash. The total percent of flash and cement was10% to investigate the variation in the effectiveness of activation. The optimum moisture content that which computed by the compaction test was adopted in the rest of the experimental program. The test results revealed that the cement could be used to improve the activating of the fly ash efficiently. The maximum value of dry density was marginally affected due to activation from 1.747 to 1.738 g/cm3 along with a corresponding change in optimum water content from 17.45 to 15.5 %. The soil cohesion parameter increased from 188 to 206 kN/m2 whereas the angle of internal friction rose from about 56.7o to 59.1o. Finally, the results of the unconfined compression test reveal that the cement-activated fly ash could present better results than those obtained from a 28-days curing cement.

Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

The check dams in grassed stormwater channels enhance infiltration capacity by temporarily blocking water flow. However, the design properties of check dams, such as their height and spacing, have a significant influence on the flow regime in grassed stormwater channels and thus channel infiltration capacity. In this study, a mass-balance method was applied to a grassed channel model to investigate the effects of height and spacing of check dams on channel infiltration capacity. Moreover, an empirical infiltration model was derived by improving the modified Kostiakov model for reliable estimation of infiltration capacity of a grassed stormwater channel due to check dams from four hydraulic parameters of channels, namely, the water level, channel base width, channel side slope, and flow velocity. The result revealed that channel infiltration was increased from 12% to 20% with the increase of check dam height from 10 to 20 cm. However, the infiltration was found to decrease from 20% to 19% when a 20 cm height check dam spacing was increased from 10 to 30 m. These results indicate the effectiveness of increasing height of check dams for maximizing the infiltration capacity of grassed stormwater channels and reduction of runoff volume.

The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used. A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.

The evaporation is one of the basic components of the hydrologic cycle and is essential for studies such as water balance, irrigation system design, and water resource management and it is requires knowledge of the values of many climatic variables. In order to estimate the evaporation, direct measurement methods or physical and empirical models can be used. Using direct methods require installing meteorological stations and instruments for measuring evaporation. Installing such instruments in various areas requires specific facilities and cost which is hard to be employed. Accordingly, this paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from Hemren reservoir by using daily temperature, relative humidity, wind velocity, sunshine hours, and evaporation data in Hemren meteorological station. Also, this study outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. The Lev. Marqn. Back Prog. (LMBP) has been utilized to construct the ANN models. For the development ANN model, different networks with different numbers of neurons and layers were evaluated. Mean Squared Error (MSE) and the Correlation Coefficient (R2) were employed to evaluate the accuracy of the proposed model. The study shows that the best model for estimation of evaporation is ANN (4-10-1), it have MSE equal to 0.112711 and the correlation coefficient (R2) equal to 0.999540.

In this study Water Quality Index (WQI) was applied in Hemren Lake, Diyala province, Iraq using ten water quality parameters (pH, Electrical Conductivity, Hardness, Total Dissolve Soluble, Sodium, Calcium, Magnesium, Potassium, Chloride, Phosphate) from 2008 to 2010 to evaluate the suitability of Hemren Lake ecosystem for drinking and irrigation uses. The Weighted Arithmetic Index method (WAM) and the Canadian Council of Ministers of the Environment Water Quality Index methodology (The CWQI 1.0 model) were used to calculate the water quality index (W.Q.I). The results indicated that drinking water quality of Hemren Lake is good and marginal for the study period according to (WAM) and (CCME) respectively, while the irrigation water quality is good and according to (WAM) and (CCME). It is suggested that monitoring of the lake is necessary for proper management. Application of the WQI is also suggested as a very helpful tool that enables the public and decision makers to evaluate water quality of lakes in Iraq.