Utilization of a bioinspired algorithm for optimum reservoir operation in an altering climate a case study of China.

Reservoir structures have been affected due to the climate challenge, which directly affects inflow conditions and water resource management. A combined model is used in the present study for predicting inflows in the future with respect to climate change This research presents an innovative framework in the management of a climate-resilient reservoir combining the Soil and Water Assessment Tool (SWAT) with Capsule Neural Networks (Caps-Net), Modified Seagull Optimization Algorithms (MSOA). Modified Seagull Optimization Algorithm (MSOA) improves reservoir Rule Curves for changing hydrology and reduced operational risks. On the other hand, this model assesses the impacts of climate change in reservoir inflows through the application of downscaled CMIP6 climate projections under Shared Socioeconomic Pathways (SSPs) and adaptive rule curves. The method described enhances water security and can be made to work in climate-variable regions. The average reduction is from 8.43 to 31.66% depending on the scenario, indicating reduced annual inflow. Working MSOA alongside conventional methods would allow for the enhancement of water storage during wetter periods and more releases during drought periods through careful organization of their rule curves. Improved reservoir rule curves are better than storing more water in the wet months (July-September) and releasing more in the dry months (December-April) which minimize floods and drought risks. MSOA-based strategies are better than conventional approaches due to their faster convergence and superior search capabilities and accuracy, hence a more robust adaptive solution to climate variance. Such changes are introduced to the rule curves based on the results of MSOA in terms of flood risk reduction and drought resilience under Shared Socioeconomic Pathways (SSPs). This is because MSOA has indeed more advantages over other optimizers, including speed increase in convergence, stronger global search capability, improved dynamic exploration-exploitation balance, and increased accuracy against multi-objective optimization problems.