Optimizing Water Resource Management Through Big Data Analytics and Artificial Intelligence: A Proposal for an Adoption Model – Case Study of HBA Souss Massa in Morocco

Big data analytics and artificial intelligence are transformative technologies driving innovation across industries, including water resource management. These technologies provide promising solutions to optimize water management globally by enabling informed, data-driven decision-making. Implementing robust big data and AI architectures in Morocco offers a strategic advantage in addressing critical water management challenges. This study presents an overview of water resource management practices in Morocco. It explores how big data analytics combined with AI can enhance operational efficiency, predict water demand, and improve resource allocation. We analyze five prominent big data and AI architectures from existing research, examining their use cases and limitations in water management contexts. Based on these insights, we propose an adoption model and a prototype framework that equips adopters with a structured approach for selecting suitable tools at each architecture stage. Our proposed model offers a customizable roadmap, empowering Moroccan water authorities to effectively deploy a comprehensive big data and AI-based management system. This approach provides a clear understanding of big data pipelines and AI integration and establishes a scalable foundation for optimized water resource management in Morocco.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-04-30
DOI
https://doi.org/10.1051/e3sconf/202670803011/pdf
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing Water Resource Management Through Big Data Analytics and Artificial Intelligence: A Proposal for an Adoption Model – Case Study of HBA Souss Massa in Morocco

Hicham Jamil, Rachid El Ansari, Elhassan Jamal, Youssef Rissouni et al.
Springer Link (Chiba Institute of Technology)
Water resources management and optimization
article

Optimizing Water Resource Management Through Big Data Analytics and Artificial Intelligence: A Proposal for an Adoption Model – Case Study of HBA Souss Massa in Morocco

Hicham Jamil, Rachid El Ansari, Elhassan Jamal, Youssef Rissouni, Aimad Tahi, Jamal Chao, Mohamed Amine Mitach, Abdelali Taouss, Bouabid El Mansouri, Aniss Moumen
article en

Abstract

Big data analytics and artificial intelligence are transformative technologies driving innovation across industries, including water resource management. These technologies provide promising solutions to optimize water management globally by enabling informed, data-driven decision-making. Implementing robust big data and AI architectures in Morocco offers a strategic advantage in addressing critical water management challenges. This study presents an overview of water resource management practices in Morocco. It explores how big data analytics combined with AI can enhance operational efficiency, predict water demand, and improve resource allocation. We analyze five prominent big data and AI architectures from existing research, examining their use cases and limitations in water management contexts. Based on these insights, we propose an adoption model and a prototype framework that equips adopters with a structured approach for selecting suitable tools at each architecture stage. Our proposed model offers a customizable roadmap, empowering Moroccan water authorities to effectively deploy a comprehensive big data and AI-based management system. This approach provides a clear understanding of big data pipelines and AI integration and establishes a scalable foundation for optimized water resource management in Morocco.

Springer Link (Chiba Institute of Technology)
Clean water and sanitation
Openalex Percentile: Top 8%
Water resources management and optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.