No abstract

No abstract

No abstract

Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.

No abstract

Meticulous prediction of hydrological processes, especially water budget, has an individual importance in environmental management plans. On the other hand, conservation of groundwater, a fundamental resource in arid and semi-arid areas, needs to be considered as a great priority in development plans. Prediction of a groundwater budget utilizing artificial intelligence was the scope of this study. For this aim, the Azarshahr Plain aquifer, East Azerbaijan, Iran, was selected because of its great dependence on groundwater and the necessity of cognizance of its budget in future programs. The long-term fluctuations of the water table in 13 piezometers were simulated by a wavelet-based artificial neural network (WANN) hybrid model, and their statistical gaps were covered. Then, the modelled water table was predicted for the next 12 months using genetic programming. The results of simulation and prediction were assessed by performance evaluation criteria such as R2, root mean squared error, mean absolute error and Nash–Sutcliffe efficiency. Thiessen polygons were then utilized, plotting the predicted unit hydrograph of the study area. The predicted water table from September 2012 to August 2013 revealed about 0.12 m depletion. Regarding the area of the Azarshahr Plain aquifer and its average storage coefficient, the aquifer budget will be reduced by about 0.3557 million cubic metres during this period.

The index-based method (GALDIT) has been established over the years for assessing the vulnerability of aquifers for saline water intrusion based on six parameters, i.e., groundwater occurrence, aquifer hydraulic conductivity, level of water table above the sea, distance from shore, impact of existing amplitude of saline water intrusion, and thickness of the aquifer. This method incorporates weight and the importance rating of factors to assess the vulnerable zones to saltwater. The objective of the present investigation is to simplify the GALDIT method as a procedure in vulnerability assessment of saltwater intrusion by substituting for the level of the water table above the sea with the more effective groundwater pumping rate in order to effectively represent the groundwater condition. Hence, the method is named as the GAPDIT method (BL^being replaced by BP^). The developed model was applied to a coastal aquifer in the hyper-saline lake vicinity of Urmia, Iran. The Analytic Hierarchy Process (AHP) was employed to enhance the prediction of vulnerability distribution. Thus, for every factor, a separate interpolated layer was plotted by empirical Bayesian kriging (EBK) method, and then, a final vulnerability map was derived overlaying the individual layers and their special weight application. The outcome revealed that the substituted pumping rate was justified in cases that water table data are not available and resultant maps showed reasonable correlation between the GALDIT and GAPDIT methods. Also, the derived maps revealed that the highest vulnerability exists in the northwest of the study area.

No abstract