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Morphometric analysis is a pertinent scientific approach in any hydrological analysis, and it is necessary in the progress and management of drainage basin. Identification of areas at risk of erosion, and the prioritization of 48 sub-watersheds of Inaouene basin, was done by using linear, relief and areal aspects of watershed. The research carried out the use of geographic information system spatial data. The linear aspects include stream number, stream sequence, stream length, and bifurcation ratio, mean length of stream order, stream length ratio, mean stream length ratio, and form factor. The areal aspect includes frequency of stream, drainage density, texture ratio, channel length constant, and overland flow maintenance length. Ultimately, the relief dimensions included relief proportion, relief and ruggedness number. The array of compound (Cp) values computed allow us to set the priority ranks and classify the sub-watershed into three priority ranks groups: low, moderate, and high priority. Such morphometric analyses can be used therefore as a watershed erosion status estimator to prioritize land and water conservation initiatives and natural resources management.

Landslides are most catastrophic and frequently occurred across the world. In mountainous areas of the globe, recurrent occurrences of landslide have caused huge amount of economic losses and a lar...

In arid and semi-arid regions, groundwater is considered being the most available natural resources for different water use. However, it is being limited in quantity. As such, its sustainable development and managementent depends on based on various criteria (e.g. climatic conditions, scale, aquifer properties, etc.). This study presents three multi-criteria index approaches (Analytic Hierarchy Process (AHP), Best–Worst Method (BWM), and Full Consistency Method (FUCOM) to classify groundwater potential maps in the Sarakhs Plain in North-east Iran. In this study, 10 parameters (layers) that affect groundwater potential recharge mapping (GPRM) are used using ArcGIS10.2. These layers includeground surface elevation, surface slope, aspect, relative slope position (RSP), plan curvature, topographic wetness index (TWI), terrain ruggedness index (TRI), drainage density, landuse, and lithology. These layers and their features were assigned properweights based on the conceptual frameworks of AHP, BWM, and FUCOM techniques, and then using a weighted overlay summation process (WOSP), final maps of groundwater potential in Sarakhs plain are obtained. The developed groundwater potential maps are classified into four classes, including low, medium, high, and very-high. The results show that among the 10 driving parameters, land use, and lithology have the highest importance and the surface slope has the lowest importance in the mapping of groundwater potential recharge. The best groundwater potential zones are concentrated in northeast and southeast, central parts, and a few parts in the areas of the western regionof the Sarakhs plain due to its nearly gentle slopes with quaternary alluvial and agriculture land and lower drainage density. The obtained results are of high value for decision-makers in the Sarakhs plain in specific and for entire Iran in general to apply sustainable groundwater utilization plans.

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were $${X}_{t-1},\; {X}_{t-2}, \;{X}_{t-3}, \;{X}_{t-4}, \; {X}_{t-12}$$ X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE $$\approx$$ ≈ 0.08 m), mean absolute error (MAE $$\approx$$ ≈ 0.06 m), and coefficient of determination (R2$$\approx$$ ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Soil erosion causes significant damage to humans by reducing soil productivity and filling reservoirs from sediment deposition in Narmada Basin, India; hence, it is important to recognize soil erosion prone areas for preventive steps in this basin. In this research, prioritization of sub-watersheds of Narmada Basin has been done using game theory-based approaches such as Condorcet and Fallback bargaining. For this purpose, Digital Elevation Model (DEM) generated by Shuttle Radar Topography Mission (SRTM) was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Based on the Condorcet and Fallback bargaining methods, the Mohgaon watershed came at the first priority ranking, that means it’s the most vulnerable watershed from the point of soil erosion (SE). Game theory was successfully implemented for prioritizing watersheds in term of SE. The findings showed that morphometric parameters and game theory approach have a high efficiency in recognizing areas that are vulnerable to erosion.

The study aims to evaluate the long-term accuracy of global precipitation (Climate Prediction Center (CPC) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Netw...

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

Can Tho City is experiencing water stress driven by rapid global changes. This study assesses the spatiotemporal variation in surface water quality (SWQ) through a multivariate statistical approach to provide evidence-based scientific information supporting sustainable water resource management and to contribute to achieving the city’s sustainable development goals (SDGs). The complex SWQ dataset with 14 monthly-measured parameters at 73 sampling sites throughout the city were collected and analyzed. The obtained results indicated that average concentrations of biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), total coliform (TC), turbidity, total suspended solids (TSS), and phosphate (PO43−) exceeded the permissible national levels. Spatially, cluster analysis (CA) was divided the city’s river basin into three different zones (mixed urban-industrial, agricultural, and mixed urban-rural zones). The key sources of SWQ pollution in these three zones were individually identified by principal component/factor analysis (PCA/FA), which were mainly related to domestic wastewater, industrial effluents, farming runoff, soil erosion, upstream sediment flows, and severe droughts. Discriminant analysis (DA) also explored that COD, DO, turbidity, nitrate (NO3−), and PO43− were the key parameters discriminating SWQ in the city among seasons and land-use zones. The temporally-analyzed results from Weighted Arithmetic Water Quality Index (WAWQI) estimation revealed the deterioration of SWQ conditions, whereby, the total polluted monitoring sites of the city, increased from 29% in 2013 to 51% in 2019. The key drivers accused of this deterioration were the expansion in built-up and industrial land areas, farming runoff, and droughts.

Modelling river water level (WL) of a coastal catchment is much complex due to the tidal influences on river WL. A hybrid machine learning model based on relevance vector machine (RVM) and improved grasshopper optimization (IGOA) is proposed in this study for modelling hourly WL in a catchment located in the east coast of tropical peninsular Malaysia. Considering the non-linear relationship between inputs and output, a recursive elimination filter based on support vector machine (SVM-RFE) was employed for the selection of the best combination of inputs from antecedent WL and rainfall data for the prediction of WL one hour ahead. The performance of IGOA was compared with classical GOA and particle swarm optimization (PSO) algorithms. Besides, the performance of the hybrid RVM model was compared with the artificial neural network (ANN) models hybridized with the same optimization algorithms. The SVM-RFE selected 1-, 12- and 24-lags WL data and 1-lag rainfall data as the most potential inputs. The relative performance of the models revealed the reliability of RVM-IGOA in WL prediction of a coastal catchment. Significant improvement of model performance was noticed after optimization using IGOA with Nash-Sutcliff Efficiency (NSE) of 0.986 and 0.981, and Kling-Gupta Efficient (KGE) of 0.981 and 0.974 for RVM-IGOA and ANN-IGOA respectively, compared to the models hybridized using other optimization algorithms with NSE between 0.969 and 0.971, and KGE between 0.890 and 0.908. The study indicates the selection of predictors based on their non-linear relations with WL and better optimization of model parameters can improve model performance in simulation of highly complex hydrological phenomena like tidal river WL in a tropical coastal catchment.

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely, Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ &gt, Na+ &gt, Mg2+ &gt, K+ and alkalinity &gt, SO42&minus, &gt, Cl&minus, NO3&minus, F&minus, The trace elements concentrations were reported in the following order: Fe &gt, Mn &gt, B &gt, Cr &gt, Pb &gt, Ni &gt, Cu &gt, Zn &gt, Cd. The surface water belongs to the Ca2+-Mg2+-HCO3&minus, and Ca2+-Mg2+-Cl&minus, water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.