一个集成的智能模型,用于模拟和优化在多种压力下区域水资源的可持续性
1School of Economics, Sichuan University of Science & Engineering, Yibin 644000, China.
Environmental research
|March 9, 2026
概括
本研究介绍了水资源稀缺地区水资源管理的综合智能模型 (IIM). 该模型通过AI预测和多系统协同优化优化经济增长和环境目标,促进可持续性.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 人工智能的人工智能
背景情况:
- 快速的城市化加剧了对区域水资源的压力,包括需求,稀缺和污染.
- 现有的框架缺乏整合长期动态预测与协调优化复杂的合系统.
研究的目的:
- 提出和应用一个综合智能模型 (IIM) 在水资源稀缺地区的水资源管理.
- 开发一种新的框架,将人工智能驱动的预测与动态的多目标优化相结合.
主要方法:
- 开发了一个集成智能模型 (IIM),将GM(1,1) 和BP神经网络预测与多目标优化系统相结合.
- 结合的社会经济,水资源和环境子系统.
- 模拟了从2018年到2035年的可持续途径的五个渐进式约束场景 (S0-S4).
主要成果:
- 情景S3,每年减少5%的地下水和3%的废水,优化经济增长 (6.73%的GRP) 和环境目标.
- 实现了59.4%的水强度降低,36%的化学氧气需求 (COD) 排放减少,以及16.0%的地下水份额.
- 确定了结构性经济转型 (第三产业为64.5%) 和废水再利用作为关键的优化机制.
结论:
- 结合人工智能预测和多系统共同优化的智能建模框架,为水资源管理提供了适应性决策支持.
- 证明了经济和环境目标之间的非线性权衡,由于过于严格的约束,边际回报减少.
- 国际水利研究所在高度压力地区促进水资源的可持续性.
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