Mastir et al. (2025) Integrating Artificial Intelligence into the Morocco Flood and Drought Monitor: A Framework for Sustainable and Resilient Disaster Management

Identification

• Journal: Lecture notes in networks and systems
• Year: 2025
• Date: 2025-11-11
• Authors: Mohamed Mastir, Aziz Dahbi, Khalil El-Hami
• DOI: 10.1007/978-3-032-02312-4_25

Research Groups

• Mohammed V University in Rabat, Scientific Institute, Rabat, Morocco

Short Summary

This paper proposes an Artificial Intelligence (AI)-integrated framework to enhance the Morocco Flood and Drought Monitor (MFDM), aiming to improve disaster preparedness, response, and prevention through predictive analytics, real-time monitoring, and decision support systems.

Objective

• To investigate the potential of integrating Artificial Intelligence into the Morocco Flood and Drought Monitor (MFDM) to develop a new generation of disaster management tools, aiming to mitigate socio-economic effects of water-borne disasters and build community resilience.

Study Configuration

• Spatial Scale: Morocco (specifically the Morocco Flood and Drought Monitor - MFDM)
• Temporal Scale: Framework proposal for ongoing disaster management

Methodology and Data

• Models used: A conceptual framework integrating predictive analytics, real-time monitoring, and decision support systems, leveraging Artificial Intelligence. Specific AI algorithms are not detailed.
• Data sources: Extensive data warehouses of the Morocco Flood and Drought Monitor (MFDM).

Main Results

• A conceptual framework is introduced for integrating Artificial Intelligence (AI) into the Morocco Flood and Drought Monitor (MFDM).
• This framework incorporates predictive analytics, real-time monitoring, and decision support systems, leveraging existing MFDM data warehouses.
• The proposed AI-enhanced system is designed to improve preparedness, response, and prevention of water-borne disasters, aiming to mitigate socio-economic impacts and build community resilience.

Contributions

• This study provides a novel framework for integrating AI into a national disaster monitoring system (MFDM), offering a structured approach to enhance disaster management capabilities in Morocco.
• It aims to evolve existing tools into a new generation of sustainable and resilient solutions, drawing on international best practices.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Mastir2025Integrating,
  author = {Mastir, Mohamed and Dahbi, Aziz and El-Hami, Khalil},
  title = {Integrating Artificial Intelligence into the Morocco Flood and Drought Monitor: A Framework for Sustainable and Resilient Disaster Management},
  journal = {Lecture notes in networks and systems},
  year = {2025},
  doi = {10.1007/978-3-032-02312-4_25},
  url = {https://doi.org/10.1007/978-3-032-02312-4_25}
}