Ehmimed et al. (2025) AI for Climate Change Resilience in Water Management

Identification

• Journal: Lecture notes in networks and systems
• Year: 2025
• Date: 2025-11-11
• Authors: Nadir Ehmimed, Mohamed Yassin Chkouri, Abdellah Touhafi
• DOI: 10.1007/978-3-032-02312-4_22

Research Groups

• SIGL Laboratory, ENSATE, Abdelmalek Essaadi University, Tetouan, Morocco
• INDI Department, Vrije Universiteit Brussel (VUB), Brussels, Belgium

Short Summary

This paper provides an in-depth, state-of-the-art analysis of Artificial Intelligence (AI) applications in water management for climate change resilience, evaluating their effectiveness, limitations, and future opportunities. It highlights the critical role of AI in improving water security, provided challenges like data scarcity, interpretability, and ethical frameworks are addressed.

Objective

• To deliver an in-depth, state-of-the-art analysis of AI applications in water management, their effectiveness, limitations, and future opportunities.
• To highlight the importance of interdisciplinary collaboration, more effective integration of data, and ethical AI framework design for sustainable and scalable solutions in climate-resilient water systems.

Study Configuration

• Spatial Scale: Global (conceptual review of AI applications in water management).
• Temporal Scale: Present and future (examining current applications and future opportunities for climate change resilience).

Methodology and Data

• Models used: The paper reviews the application of various AI technologies, including Machine Learning (ML), Digital Twins, and Decision Support Systems (DSS), in water management.
• Data sources: The paper discusses the use of remote sensing analytics and various hydrological and water quality data as part of AI applications, while also identifying data scarcity and quality as critical challenges.

Main Results

• AI offers advanced solutions for hydrological modeling, water resource optimization, and disaster preparedness, crucial for sustainable water management under climate change.
• Key AI technologies like digital twins, machine learning, remote sensing analytics, and decision support systems enhance predictive accuracy and enable adaptive management strategies.
• Significant challenges for AI implementation in water systems include data scarcity, data quality issues, interpretability challenges, computational power constraints, and regulatory barriers.
• Overcoming these challenges requires interdisciplinary collaboration, effective data integration, and the design of ethical AI frameworks to ensure sustainable and scalable solutions for water security and climate resilience.

Contributions

• Provides a comprehensive, state-of-the-art review of AI applications specifically tailored for climate change resilience in water management.
• Systematically identifies and synthesizes the effectiveness, current limitations, and future opportunities for AI in this critical domain.
• Emphasizes the crucial need for interdisciplinary collaboration, robust data integration strategies, and ethical AI framework development to unlock AI's full potential in water security.

Funding

• No specific funding projects, programs, or reference codes were listed in the paper.

Citation

@article{Ehmimed2025AI,
  author = {Ehmimed, Nadir and Chkouri, Mohamed Yassin and Touhafi, Abdellah},
  title = {AI for Climate Change Resilience in Water Management},
  journal = {Lecture notes in networks and systems},
  year = {2025},
  doi = {10.1007/978-3-032-02312-4_22},
  url = {https://doi.org/10.1007/978-3-032-02312-4_22}
}