Urban water resource management for sustainable environment planning using artificial intelligence techniques

水资源综合管理 水资源 环境规划 可持续发展 过程(计算) 环境资源管理 资源管理(计算) 持续性 资源(消歧) 环境科学 计算机科学 环境经济学 生态学 生物 操作系统 经济 计算机网络
作者
Xiaojun Xiang,Qiong Li,Shahnawaz Khan,Osamah Ibrahim Khalaf
出处
期刊:Environmental Impact Assessment Review [Elsevier BV]
卷期号:86: 106515-106515 被引量:481
标识
DOI:10.1016/j.eiar.2020.106515
摘要

In the current era, water is a significant resource for socio-economic growth and the protection of healthy environments. Properly controlled water resources are considered a vital part of development, which reduces poverty and equity. Conventional Water system Management maximizes the existing water flows available to satisfy all competing demands, including on-site water and groundwater. Therefore, Climatic change would intensify the specific challenges in water resource management by contributing to uncertainty. Sustainable water resources management is an essential process for ensuring the earth's life and the future. Nonlinear effects, stochastic dynamics, and hydraulic constraints are challenging in ecological planning for sustainable water development. In this paper, Adaptive Intelligent Dynamic Water Resource Planning (AIDWRP) has been proposed to sustain the urban areas' water environment. Here, an adaptive intelligent approach is a subset of the Artificial Intelligence (AI) technique in which environmental planning for sustainable water development has been modeled effectively. Artificial intelligence modeling improves water efficiency by transforming information into a leaner process, improving decision-making based on data-driven by combining numeric AI tools and human intellectual skills. In AIDWRP, Markov Decision Process (MDP) discusses the dynamic water resource management issue with annual use and released locational constraints that develop sensitivity-driven methods to optimize several efficient environmental planning and management policies. Consequently, there is a specific relief from the engagement of supply and demand for water resources, and substantial improvements in local economic efficiency have been simulated with numerical outcomes.
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