Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.
J Environ Manage. 2022 Aug 1;315:115073. doi: 10.1016/j.jenvman.2022.115073. Epub 2022 May 4.
In this paper, a hybrid method integrated unbiased grey model (UGM) and artificial neural network (ANN) into an interval two-stage fuzzy credibility-constrained programming (ITFCP) framework is proposed for water resources allocation of the Yalong River research area. Through the grey correlation analysis and the eXtreme Gradient Boosting (XGboost) algorithm, the economic and social indicators are related to the water demands of different water sectors in different regions can be obtained for building water demand prediction model. According to the unbiased grey prediction of the socio-economic development data of each region in the Yalong River Basin (YRB), water demand prediction models are constructed by using neural network. The establishment of a hybrid two-stage interval fuzzy credibility-constrained programming model can analyze the uncertainties existing in the process of water resources allocation. Taking 2020, 2025, and 2030 as the planning years, the developed model studies and reveals the system benefits at different credibility levels, the water shortage of each user in sub-regions and the water resources allocation situation to provide suggestion for managers to optimize the allocation of water resources. Compared to the previous methods, this integrated model can help decision-makers set management policies more sustainably and profitably.
本文提出了一种将无偏灰色模型（UGM）和人工神经网络（ANN）集成到区间两阶段模糊置信约束规划（ITFCP）框架中的混合方法，用于雅砻江研究区的水资源分配。通过灰色关联分析和极端梯度提升（XGboost）算法，可以获得与不同地区不同水部门的用水需求相关的经济和社会指标，以建立用水需求预测模型。根据雅砻江流域（YRB）各地区社会经济发展数据的无偏灰色预测，利用神经网络构建用水需求预测模型。建立混合两阶段区间模糊置信约束规划模型可以分析水资源分配过程中存在的不确定性。以 2020 年、2025 年和 2030 年为规划年份，开发的模型研究和揭示了不同置信水平下的系统效益、各子区域用户的缺水情况和水资源分配情况，为管理者优化水资源配置提供建议。与以前的方法相比，该综合模型可以帮助决策者更可持续和更有利地制定管理政策。
