In ecologically fragile karst regions, surface water leakage and spatial mismatches between supply and demand exacerbate water scarcity and ecosystem degradation. In this context, sustainable water resource allocation is of great significance for achieving the United Nations Sustainable Development Goals (SDGs). This study proposes a Dual-stage Prediction and Optimization Coupled Allocation Model (DPOCAM), which integrates an LSTM–Transformer-based intelligent water demand forecasting model with the NSGA-III multi-objective optimization algorithm. The forecasting model was trained on data from 2001 to 2020 and tested on data from 2021 to 2024, achieving a mean absolute percentage error of 2.89%. The model incorporates ecological water demand as an independent optimization objective, quantified using the Tennant method, aiming to coordinate the relationship between domestic and productive water use with aquatic ecosystem protection. Applied to Sinan County, a typical karst area in Guizhou Province, China, the model projects sectoral water demands for 2035 and conducts water resource allocation based on water network planning. Results show that under the current water network, the comprehensive water shortage rate reaches 17.7%, with ecological deficit accounting for 10.1%, posing dual threats to human water security and ecosystem integrity. Following the planned construction of a water network centered on the Huatanzi Reservoir, the overall shortage rate drops to 0.6%, and the ecological deficit declines to 4.6%, demonstrating significant improvements in both water supply reliability and ecological flow guarantee. The water network construction plays a positive role in reducing water shortage rates and enhancing ecological flow protection, providing a scientific basis and practical reference for sustainable water resource management in karst regions.
Gao et al. (Tue,) studied this question.
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