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The low water storage capacity caused water crisis in Pakistan; therefore, the country needs both small- and large-scale reservoirs to store surplus water resources. The construction of large dams in Pakistan could not be materialized due to financial and political constraints. However, the construction of multi-purpose small dams is the next best option to store water. To this end, there is a need to identify the best feasible sites in the country. The selection of feasible sites for multi-purpose dams must take into account multiple criteria, including engineering, socioeconomic and environmental. The current study utilizes the coupled Remote Sensing and Geographical Information System techniques to identify the feasible sites for multi-purpose small dams, considering the socioeconomic and environmental criteria in addition to the established engineering criteria in district Swat, Khyber Pakhtunkhwa. The suitability map considered nine engineering criteria, including rainfall distribution, slope, land use, curve number, runoff depth, soil, alluvial depth, closeness to streams, and drainage density, using the weights calculated from priority indices. The suitability map is divided into four classes, i.e., excellent, good, moderate, and unsuitable with the excellent and good classes area of 66.78 km2, and 195.75 km2. Twenty sites (based on accessibility and closeness to Swat River) from each class are selected that are situated in Kalam, Babozai, Bahrain, Madyan, Khwazakhela, Matta, Kabal, and Barikot areas of district Swat and evaluated using socioeconomic and environmental criteria, i.e., community well and no displacement cost, management ownership–private or public, biodiversity protection services, instrumental in groundwater recharge for the community, electricity generation for the local community, low maintenance cost, flood friendly, appropriate distribution of water resources, political well, and irrigation and drinking water potential. The top priority for these areas is electricity generation, flood protection, irrigation and drinking water capability, sustainable operation, low maintenance cost and political well. The current study demonstrated that socioeconomic and environmental criteria augment the engineering approach in identifying the best feasible site for multi-purpose small dams. These sites would not only store the water but would also provide important services (electricity generation, irrigation water, etc.) to the local community and economy. [ABSTRACT FROM AUTHOR]

Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535–0.965, MAE = 0.363–0.622, NSE = 0.558–0.860, COC = 0.760–0.930, and WI = 0.862–0.959) outperformed the SVR-PSO model (RMSE = 0.546–0.967, MAE = 0.372–0.625, NSE = 0.556–0.855, COC = 0.758–0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area. [ABSTRACT FROM AUTHOR]

Drought is a complex natural phenomenon, so, precise prediction of drought is an effective mitigation tool for measuring the negative consequences on agriculture, ecosystems, hydrology, and water resources. The purpose of this research was to explore the potential capability of support vector regression (SVR) integrated with two meta-heuristic algorithms i.e., Grey Wolf Optimizer (GWO), and Spotted Hyena Optimizer (SHO), for meteorological drought (MD) prediction by utilizing EDI (effective drought index). For this objective, the two-hybrid SVR–GWO, and SVR–SHO models were constructed at Kumaon and Garhwal regions of Uttarakhand State (India). The EDI was computed in both study regions by using monthly rainfall data series to calibrate and validate the advanced hybrid SVR models. The autocorrelation function (ACF) and partial-ACF (PACF) were utilized to determine the optimal inputs (antecedent EDI) for EDI prediction. The results produced by the hybrid SVR models were compared with the calculated (observed) values by employing the statistical indicators and through graphical inspection. A comparison of results demonstrates that the hybrid SVR–GWO model outperformed to the SVR–SHO models for all study stations located in Kumaon and Garhwal regions. Also, the results highlighted the better suitability, supremacy, and convergence behavior of meta-heuristic algorithms (i.e., GWO and SHO) for meteorological drought prediction in the study regions. [ABSTRACT FROM AUTHOR]

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000–2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin,Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region. [ABSTRACT FROM AUTHOR]