Yang Mengzhuo, Gao Erkun, Wang Gaoxu, Li Daiyuan, Zhou Wenqi, Zhou Xingchi
Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China.
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China.
Sci Rep. 2024 Nov 13;14(1):27763. doi: 10.1038/s41598-024-78980-5.
Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.
精确的需水量预测对于区域水资源的高效配置和合理利用至关重要。本研究通过引入一种新型耦合模型HHO-BPNN（哈里斯鹰优化-反向传播神经网络）应对中长期需水量预测相关挑战。采用主成分分析来降低潜在需水因素的维度。通过均方误差（MSE）、平均绝对百分比误差（MAPE）、平均绝对误差（MAE）和决定系数（R）对预测模型的性能进行比较。研究结果表明，HHO-BPNN优于传统方法，如BPNN、支持向量机和灰色预测模型。该研究利用滑动窗口法对中国江苏省北部五个地级市未来1年、3年和5年的需水量进行预测。在各类需水（农业、工业、生活和生态）方面均实现了较高的预测精度，总体精度高达97%，令人印象深刻。此外，预测结果与当地发展规划吻合良好，表明其在城市规划中具有实际适用性。本研究阐明了影响需水量的关键驱动因素，为区域需水量预测提供了有效工具，有助于未来实现高效精确的水资源管理和决策。
