The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Urban water resource management for sustainable environment planning using artificial intelligence techniques
Abstract
Abstract 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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Cites
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Kamrowska-Załuska, D. (2021) · Land · doi:10.3390/land10111209 · Cited by 95
A systematic review of current AI techniques used in the context of the SDGs
Greif, L., Röckel, F., Kimmig, A., & Ovtcharova, J. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-024-00668-5 · Cited by 55
Machine learning at the edge for AI-enabled multiplexed pathogen detection
Ganjalizadeh, V., Meena, G. G., Stott, M. A., Hawkins, A. R., & Schmidt, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31694-6 · Cited by 24
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Metadata
- Authors
- Xiang, X., Li, Q., Khan, S., & Khalaf, O. I.
- Venue
- Environmental Impact Assessment Review
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 86
- Pages
- 106515
- ISSN
- 0195-9255 (ISSN-L)
- OpenAlex
- W3099865851
- Cited by
- 533
- References
- 23
- FWCI
- 31.31
- Type
- journal article
- Language
- en
- Open access
- closed
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