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In addition to global warming and population growth, the Turkish and Iranian water projects have negatively affected the water resources in Iraq. Recently, Iran has implemented its largest water project (The Tropical Water Project – TWP). This project aims to store the flowing water in the Sirwan and Zmkan rivers (i.e., tributaries of the Diyala River) to provide irrigation, domestic, and industrial water for areas inside and outside the Diyala River Basin. The Darbandikhan dam and Halabja water supply project are located on the Diyala River. In this study, the impact of the TWP on the operation of the Darbandikhan dam and Halabja water supply project was investigated. The monthly inflow data of the Darbandikhan dam from September 1991 to December 2017 were used to simulate the operation of the dam using the HEC-ResSim 3.1 package. Two scenarios were adopted for the operation of the Darbandikhan dam. In the first scenario, the Darbandikhan dam operation was simulated without considering the effects of the TWP project. In contrast, in the second scenario, the full operation of the TWP project was considered. The results revealed that about 48.4% of the power generation capacity would be lost, and the water surface elevation in the reservoir would be affected significantly due to the TWP project. The water surface elevation would be at or below 443 m for long periods, which will affect the operation of the Halabja water supply project. [ABSTRACT FROM AUTHOR]

In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (f p). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (f m), concrete block compressive strength (f b), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively. [ABSTRACT FROM AUTHOR]

The infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m3/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management. [ABSTRACT FROM AUTHOR]

In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potential of the proposed advanced artificial intelligence (AI) models. The sunflower optimization algorithm is implemented to find the optimal tuning parameters. The results indicated that the ANFIS-SO model with the combination of three lags of rainfall and temperature as input attributes attained the best predictability performance. The minimal values of the root mean square error were RMSE = 1.89 m and 1.92 m for the training and testing modeling phases, respectively. The worst prediction capacity was attained for the long lead (i.e., six months rainfall lag times). The uncertainty analysis showed that the ANFIS-SO model had less uncertainty based on the percentage of more responses in the confidence band and lower bandwidth. Also, different scenarios of water harvesting were investigated with the consideration of environmental restrictions and fair water allocation to stakeholders. Further, studying Urmia Lake water harvesting scenarios displayed that the 30% water harvesting scenario of the lake water improves the lake's water level. [ABSTRACT FROM AUTHOR]

Here, batch study for Iraqi bentonite-cadmium aqueous solution interaction was conducted with conditions of contact time ≤ 120 min, sorbent dosage from 0.05 to 1 g per 100 mL and shaking speed from 50 to 300 rpm for initial concentration of 50 mg/L with initial pH of 3 to simulate the acetogenic phase in the sanitary landfill. The best conditions were 1 h, 0.7 g/100 mL and 250 rpm, respectively to obtain the highest removal (90%). The sorption data were well formulated by Freundlich and Langmuir models with determination coefficient (R2) ≥ 0.98 and sorption capacity of 145.3 mg/g; so, the removal process governed by the physico-chemical forces. The average coefficient of the hydraulic conductivity was found equal to 1.98 × 10−10 m/s and this is suitable for low permeable barrier (LPB). Finally, the predictions of COMSOL package were presented satisfactory simulation for the cadmium concentrations within two-dimensional physical model packed with sand aquifer and Iraqi bentonite. Two configurations of LPB were evaluated for restriction of contaminant front migration and the results proved that the surrounding of the location required to protect it from three sides against to the flow direction can be more dependable from continuous configuration. [ABSTRACT FROM AUTHOR]

Re-use of the byproduct wastes resulting from different municipal and industrial activities in the reclamation of contaminated water is real application for green projects and sustainability concepts. In this direction, the synthesis of composite sorbent from the mixing of waterworks and sewage sludge coated with new nanoparticles named "siderite" (WSSS) is the novelty of this study. These particles can be precipitated from the iron(II) nitrate using waterworks sludge as alkaline agent and source of carbonate. Characterization tests using X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) mapping revealed that the coating process was correctly achieved and siderite nanoparticles were planted on the surface of the composite sorbent. Interaction between Congo Red dye and composite sorbent was evaluated through a set of batch tests under the variation of agitation time, pH of aqueous solution, and sorbent dosage. The results proved that the prepared sorbent had a high ability in the treatment of water contaminated with Congo Red dye in comparison with previous studies and the maximum adsorption capacity reached to maximum value i.e. 9416 mg/g. The sorption process was governed by electrostatic attractions; however, Sips and Pseudo-second-order models described this process with coefficient of determination greater than 0.99. [ABSTRACT FROM AUTHOR]

This study investigates the capability of furnace bottom ash (FBA) as a filter media for phosphorus removal from wastewater effluents. Lab-scale studies were conducted, in which a mass of FBA was brought into contact with the phosphate solution (a predominant form of phosphorus in wastewater) in batch experiments and lab-scale filter. Intraparticle Diffusion Model (IPD) was applied and revealed that the mechanism of phosphate adsorption onto FBA consist of three phases; film phase occurred in the external surface and intraparticle diffusion phases take place in the wide pores and microspores. A Point Zero Charge (PZC) study was conducted and indicated that the FBA acts as anion exchanger and develops negative charge when the pH more than 9. Based on the PZC outcomes the FBA efficiency was regenerated by two washing methods (continuous and intermittent) with deionized water solution at pH 9.5 and 10. The intermittent washing method gave better results as the FBA regenerate 95% of its efficiency. FBA was achieved the phosphate permissible limit (0.1 mg/L) in synthetic solution at 150 min. While, in real wastewater sample was achieved it in 180 min. The FBA washing method based on PZC can be adopted as a strategy for absorbents efficiency regeneration and phosphorus recovery. [ABSTRACT FROM AUTHOR]

Utilizing the waterworks sludge byproduct in the treatment of wastewater contained methylene blue dye is one approach that has been taken in an effort to lessen the difficulties that are associated with managing such byproduct. The prime aim of this work is manufacturing of novel sorbent from co-precipitation of magnesium oxide nanoparticles on the surfaces of waterworks sludge in the existence of cetyl trimethyl ammonium bromide surfactant. Surfactant 0.04 g/50 mL, dose of sludge 2 g/50 mL, and pH 12 were the most efficient preparation parameters to remove 75.31% of adopted dye. The adsorption studies were conducted under various conditions of contact time (0–240 min), concentration of dye (10–300 mg/L), sorbent mass (0.05–1.5 g), and solution pH (3–12). The best values of batch parameters were identical to the highest percentages of contaminant removal. Results proved that the magnesium oxide nanoparticles are attached to the sludge surfaces. Freundlich and pseudo-second-order models have perfectly described sorption results with 59.92 mg/g maximum sorption capacity. The breakthrough curves can be accurately described by the Bohart-Adams model. The outputs of continuous tests have been paved the way for future usage of the prepared sorbent in the field permeable reactive barrier technology. [ABSTRACT FROM AUTHOR]

Water demands have increased even more in recent decades because of the high population density. Surface and groundwater resources are insufficient to meet these demands. As a result, governments have turned to the treatment of sewage water. Sewage water contains multiple types of contamination, creating a major health risk. In the research region, 48 water samples were obtained, including 18 samples of surface water and 30 samples of groundwater. The Canadian Council Water Quality Index (CCWQI) program calculates the water quality index to evaluate the water quality for drinking and human use. The World Health Organization (WHO) and the Egyptian Ministry of Health (EMH) determined regulatory limits for drinking water and each value of the investigated parameter connected with them. According to the findings, 79% of the tested water samples are safe to drink and are excellent for human and wildlife use. Due to infiltration or recharging of groundwater with drainage water, as well as the involvement of dissolution, leaching processes, and anthropogenic activities that damage human health, animals, and some plants, these samples are unfit for drinking and domestic consumption. The heavy metal level of Cd and Pb in the examined water samples was found to be above WHO and EMH acceptable limits. Furthermore, due to oral exposures, the examined water samples may cause complex health concerns such as non-carcinogenic and carcinogenic influences for children over adults due to a reduction in children's immunity. As a result, water treatment should be carried out in the examined region to protect the health of the residents. [ABSTRACT FROM AUTHOR]

The storage capacity of reservoirs is gradually reduced due to sediment accumulation that causes changes in the area-storage capacity (ASC) curves. Establishing these curves and predicting their future change is an important issue for planners, designers and operators of dams. Many empirical and semi-empirical approaches have been suggested for establishing and predicting the future changes for these curves. In this study four empirical and semi-empirical methods were evaluated and three of them were modified to be used for the prediction of changes in the ASC curves due to sedimentation, based on the existing sedimentation survey data for 11 reservoirs in the USA. For evaluation, these approaches were reviewed and used to determine sedimentation depth and establishing the ASC curves for the Mosul dam reservoir (MDR), which is the biggest hydraulic structure on the River Tigris in northern Iraq. MDR started operating in 1986 with a storage capacity of 11.11 km 3 and a water surface area 380 km 2 at normal operation stage (330 m a.s.l.). The results obtained from these methods were evaluated using observed bathymetric survey data that had been collected in 2011 after 25 years of the operation of the dam. The evaluation results showed three methods had presented more accurate results for estimating water depth or sedimentation depth at dam site with percentage error about 1.06–3.30%. Whilst for establishing ASC curves, one method presented good agreement result with survey data. Furthermore, ASC and sedimentation depths at dam site of MDR for periods 50, 75, 100 and 125 years were estimated using the modified approaches and the area reduction method. The results of the modified methods provided reasonable agreement when compared with the area reduction method proposed by the U.S. Bureau of Reclamation and the agreement became better with an increase in time period. [ABSTRACT FROM AUTHOR]

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

High-velocity runoff from snow deposit transports suspended grain-attached contaminants while underground snow storages trapped these contaminants within the storage. The aim here is to quantify pollutant masses from an urban snow deposit and to investigate the conditions when pollutant control was increased by turning a snow deposit into a snow cooling plant with permeable underground snow storage. Pollutant masses in an urban snow deposit in northern Sweden were: Cu = 67, Pb = 17, Zn = 160, P = 170, SS = 620,000, Cl = 1,200, N = 380 kg. A theoretical analysis showed that the fraction of surface runoff from a surface deposit largely depends on the hydraulic conductivity (K, m s-1) of the soil. For a melt rate of 30 mm, day-1, surface runoff would be about 97% for a soil with K = 10-8, while nonexistent for K>10-6. Similar soil conductivities are needed to ensure that all snow melt could be transported as groundwater from an underground storage. The largest pollution-control advantage with underground snow storage compared to a surface deposit would thus be that piping and filters for operation of the plant could be used to filter surface snow melt runoff before rejection. [ABSTRACT FROM AUTHOR]

The increasing frequency of urban floods worldwide due to rapid urbanization, frequent climatic extremes, or poor drainage conditions necessitates evaluating the performance of the urban drainage systems (UDS) and enhancing their resilience. In this study, a comprehensive assessment of the UDS of Gurugram City, India, through the concepts of sustainable drainage systems (SuDS) is presented. A stormwater management model (SWMM) was set up to model the existing UDS response to a design storm of a 5-year return period. The increase in percentage imperviousness (due to urbanization) and rainfall intensity (due to climate change) are considered the governing factors for functional failures. The results revealed climate change to be a more severe threat to UDS than urbanization, while their combinations can further worsen the repercussions. The structural failure was modelled using the single link-failure scenarios, where 3 and 12 conduits possessed low resilience and no resilience (severe), respectively. The role of SuDS in enhancing the resilience of UDS was assessed by simulating all these functional and structural failure scenarios for three SuDS-implemented conditions, i.e., only infiltration trenches (SuDS IT), only retention ponds (SuDS RP), and both of them together (SuDS IT+RP). The SuDS abated the flood magnitudes, delayed the time to peak flow, and stored an additional volume of water within the catchment, thereby justifying their efficacy to mitigate the pluvial flood and enhance the resilience of UDS. The findings of this study encourage implementing SuDS over the developing countries to bring down the frequency of urban floods. • The urban drainage system (UDS) of Gurugram City is comprehensively evaluated. • Functional and structural failure scenarios were analysed for their effects on resilience of UDS. • Compared the impacts of increase in imperviousness and rainfall intensity on UDS failure, and the latter is found to be a more serious threat. • The vulnerable conduits in the drainage networks of the city are identified. • The SuDS, viz., infiltration trenches and retention ponds were considered for mitigating the pluvial flood and enhancing the resilience of UDS. • SuDS has managed to delay and reduce the peak outflow from the catchment, assisting the catchment to store more water within it for sustainable use. [ABSTRACT FROM AUTHOR]

The establishment of water quality prediction models is vital for aquatic ecosystems analysis. The traditional methods of water quality index (WQI) analysis are time-consuming and associated with a high degree of errors. These days, the application of artificial intelligence (AI) based models are trending for capturing nonlinear and complex processes. Therefore, the present study was conducted to predict the WQI in the Kinta River, Malaysia by employing the hybrid AI model i.e., GA-EANN (genetic algorithm-emotional artificial neural network). The extreme gradient boosting (XGB) and neuro-sensitivity analysis (NSA) approaches were utilized for feature extraction, and six different model combinations were derived to examine the relationship among the WQI with water quality (WQ) variables. The efficacy of the proposed hybrid GA-EANN model was evaluated against the backpropagation neural network (BPNN) and multilinear regression (MLR) models during calibration, and validation periods based on Nash–Sutcliffe efficiency (NSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) indicators. According to the results of appraisal the hybrid GA-EANN model produced better outcomes (NSE = 0.9233/ 0.9018, MSE = 10.5195/ 9.7889 mg/L, RMSE = 3.2434/ 3.1287 mg/L, MAPE = 3.8032/ 3.0348 mg/L, and CC = 0.9609/ 0.9496) in calibration/ validation phases than BPNN and MLR models. In addition, the results indicate the better performance and suitability of the hybrid GA-EANN model with five input parameters in predicting the WQI for the study site. • Predicting the WQI for the Kinta River, Malaysia. • Efficiency of GA-EANN model was evaluated against BPNN and MLR. • XGB and NSA was employed for feature extraction. [ABSTRACT FROM AUTHOR]

Phosphate (PO 4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO 4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO 4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO 4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO 4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems. [ABSTRACT FROM AUTHOR]

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

In Jordan, like in other semi-arid countries in the region, water resources are increasingly becoming an important factor in its stability, growth, and national security,. This study examines the rainfall record of 25 stations during the period 1967-1995 to determine periodicity and interrelations between stations using power spectral, harmonic analysis, and correlation coefficient techniques. The ARIMA model is used to forecast rainfall trends in individual stations up to the year 2020. The outcomes show that the intensity of rainfall has been decreasing with time since 1967, and this trend is likely to continue into the future. The average annual rainfall is relatively low (90 mm/y), and there are some significant differences in rainfall values between the stations due mainly to topography. Stations with the same altitude that are close geographically tend to have a strong statistical association and show a similar rainfall trend. Furthermore, a statistically significant relationship was established between vegetation index (derived from remotely sensed data) and rainfall intensity. The outcomes from this study can be used as a basis for planning future agricultural, economic, and social development in Jordan and in the Badia region, in particular. [ABSTRACT FROM AUTHOR]