塞古拉水文盆地的综合水资源管理:一种人工智能方法
Urtzi Otamendi1, Mikel Maiza2, Igor G Olaizola2
1Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián 20009, Spain; Department of Computer Sciences and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián 20018, Spain.
Journal of environmental management
|October 2, 2024
概括
本研究引入了人工智能驱动的可持续水资源管理框架,优化在不确定的需求和复杂政策下分配. 这种方法成功地减少了西班牙塞古拉盆地的赤字和二氧化碳排放.
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:
- 有效的资源管理面临来自不确定需求,可变可用性和复杂治理的挑战.
- 水资源管理需要综合的方法来预测和优化.
研究的目的:
- 提出一个水资源管理框架,整合物理建模,遥感和人工智能.
- 为了优化水资源分配,预测可用性,并估计需求.
主要方法:
- 使用了全面的水文模型,农业作物模型和混合整数线性编程 (MILP).
- 集成先进的物理建模,遥感和人工智能 (AI) 算法.
- 将框架应用于西班牙塞古拉水文盆地.
主要成果:
- 在六个月内成功分配了6.42亿立方米 (hm3) 的水.
- 将水资源赤字降至最低,达到总估计需求的9.7%.
- 证明了环境效益,包括减少二氧化碳排放.
结论:
- 由人工智能驱动的框架支持可持续水资源管理的知情决策.
- 该方法可将其推广到其他盆地,增强治理和政策.
- 该方法已被验证并纳入塞古拉盆地的运营水资源管理.
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