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Abstract
This study examines the influence of climate change on hydrological processes, particularly runoff, and how it affects managing water resources and ecosystem sustainability. It uses CMIP6 data to analyze changes in runoff patterns under different Shared Socioeconomic Pathways (SSP). This study also uses a Deep belief network (DBN) and a Modified Sparrow Search Optimizer (MSSO) to enhance the runoff forecasting capabilities of the SWAT model. DBN can learn complex patterns in the data and improve the accuracy of runoff forecasting. The meta-heuristic algorithm optimizes the models through iterative search processes and finds the optimal parameter configuration in the SWAT model. The Optimal SWAT Model accurately predicts runoff patterns, with high precision in capturing variability, a strong connection between projected and actual data, and minimal inaccuracy in its predictions, as indicated by an ENS score of 0.7152 and an R2 coefficient of determination of 0.8012. The outcomes of the forecasts illustrated that the runoff will decrease in the coming years, which could threaten the water source. Therefore, managers should manage water resources with awareness of these conditions.
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1 Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou, Hainan, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302); Hainan University, School of Ecology and Environment, Haikou, Hainan, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302)
2 Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou, Hainan, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302); Hainan Qingxiao Environmental Testing Co., Ltd, Sanya, Hainan, China (GRID:grid.428986.9)
3 Hainan University, School of Ecology and Environment, Haikou, Hainan, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302); Hainan Qianchao Ecological Technology Co., Ltd, Sanya, Hainan, China (GRID:grid.428986.9)
4 Chittagong University of Engineering and Technology, Chittagong, Bangladesh (GRID:grid.442957.9) (ISNI:0000 0004 0371 3778); The Islamic University, College of Technical Engineering, Najaf, Iraq (GRID:grid.444971.b) (ISNI:0000 0004 6023 831X)