Farooq et al. (2025) Applications of Artificial Intelligence and Remote Sensing in Water Resources Management

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Advances in geospatial technologies book series
• Year: 2025
• Date: 2025-10-17
• Authors: Wasiq Farooq, Muhammad Safdar, Hamza Anjum, Muhammad Adnan Shahid, Muhammad Sajid Mehmood, Ashi Rashid, Abdul Rauf, Fahd Rasul, Imran Shauket, Naeem Saddique, Hafiz Muhammad Bilawal Akram, Muntaha Munir
• DOI: 10.4018/979-8-3373-6608-1.ch006

Research Groups

Not explicitly stated in the provided text, as this appears to be a review chapter discussing general concepts and applications rather than a specific experimental study by a single group.

Short Summary

This chapter explores the transformative potential of artificial intelligence (AI) in water resource management, detailing its applications in various assessment practices and highlighting the progress and persistent challenges in its deployment, particularly in regions like Pakistan.

Objective

• To discuss the need for improved water resource management and explore the potential of artificial intelligence (including machine learning, deep learning, and hybrid models) in reshaping water management systems and assessment practices globally, identifying both opportunities and challenges.

Study Configuration

• Spatial Scale: Ranging from watershed-level monitoring to regional deployments (e.g., Pakistan) and global applicability (developed and developing regions).
• Temporal Scale: Encompasses short-term forecasting (e.g., early warning systems, drought/flood forecasting, streamflow forecasting) to longer-term analysis (e.g., aquifer recharge analysis, hydrological anomaly detection).

Methodology and Data

• Models used: Artificial intelligence (AI), including machine learning, deep learning, and hybrid models.
• Data sources: Implied reliance on hydrological and environmental data, with an emphasis on integration with remote sensing and geographic information systems (GIS) for spatial-temporal monitoring. Data scarcity is identified as a challenge.

Main Results

• AI offers significant potential for improving water assessment practices, including rainfall-runoff modeling, groundwater prediction, drought/flood forecasting, streamflow forecasting, aquifer recharge analysis, and hydrological anomaly detection.
• The integration of AI with remote sensing and GIS is crucial for effective spatial-temporal watershed monitoring.
• Pakistan has demonstrated significant progress in deploying AI for early warning systems and water governance.
• Key challenges for AI adoption in water management include data scarcity, model overfitting, computational complexity, and skill gaps.
• There is a critical need for scalable, transferable, policy-supported, and explainable AI solutions for water management in both developed and developing regions.

Contributions

• Provides a comprehensive overview of AI's potential applications across various facets of water resource management.
• Identifies critical challenges hindering the widespread and effective adoption of AI in water management, especially in data-scarce environments.
• Emphasizes the importance of integrating AI with remote sensing and GIS for enhanced monitoring capabilities.
• Highlights the need for scalable, transferable, policy-supported, and explainable AI solutions to ensure practical and equitable implementation globally.

Funding

Not explicitly stated in the provided text.

Citation

@article{Farooq2025Applications,
  author = {Farooq, Wasiq and Safdar, Muhammad and Anjum, Hamza and Shahid, Muhammad Adnan and Mehmood, Muhammad Sajid and Rashid, Ashi and Rauf, Abdul and Rasul, Fahd and Shauket, Imran and Saddique, Naeem and Akram, Hafiz Muhammad Bilawal and Munir, Muntaha},
  title = {Applications of Artificial Intelligence and Remote Sensing in Water Resources Management},
  journal = {Advances in geospatial technologies book series},
  year = {2025},
  doi = {10.4018/979-8-3373-6608-1.ch006},
  url = {https://doi.org/10.4018/979-8-3373-6608-1.ch006}
}