Abstract
This study presents a novel approach to predicting water stress in Yichang City, China, through an optimized Capsule Neural Network (CapsNN) model enhanced by the Improved Aquila Optimization (IAO) algorithm. The model, applied to monthly water stress data from 2000 to 2022, is gauged using the Streamflow Drought Index (SDI). When compared to traditional metaheuristic models, our CapsNN/IAO framework demonstrates superior accuracy in drought forecasting across various time scales with significant reductions in The Mean Absolute Error is 0.1021, the Root Mean Square Error is 0.1824, and good the Nash-Sutcliffe Efficiency is 0.75, the coefficient of determination R2 is 0.89, and the Willmott's Index is 0.93. Notably, the model predicts short-term water shortages with greater frequency at the SDI3 scale, while identifying the most severe drought conditions at the SDI6 scale. These insights are critical for enhancing water resource management.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
