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Applications of Artificial Intelligence and Remote Sensing in Water Resources Management
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Author(s): Wasiq Farooq (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan), Muhammad Safdar (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan), Hamza Anjum (Department of Computer Science, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Khyber Pakhtunkhwa, Pakistan), Muhammad Adnan Shahid (Agricultural Remote Sensing Lab, University of Agriculture, Faisalabad, Pakistan), Muhammad Sajid Mehmood (School of Tourism and Planning, Pingdingshan University, Pingdingshan, China), Amina Rashid (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan), Abdul Rauf (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan), Fahd Rasul (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan), Imran Shauket (Department of Structures and Environmental Engineering, University of Agriculture, Faisalabad, Pakistan), Naeem Saddique (Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan), Hafiz Muhammad Bilawal Akram (Department of Agronomy, University of Agriculture, Faisalabad, Pakistan)and Muntaha Munir (Institute of Botany, University of the Punjab, Lahore, Pakistan)
Copyright: 2026
Pages: 28
Source title:
Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch006
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Abstract
The chapter discusses the need for improved water resource management due to climate change, population growth, and freshwater issues. The potential of artificial intelligence, including machine learning, deep learning, and hybrid models, in reshaping water management systems. The chapter explores AI-powered solutions for improved 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 geographic information systems is particularly important for enabling spatial-temporal watershed monitoring. Pakistan's AI deployments in early warning systems and water governance have made significant progress, but challenges like data scarcity, model overfitting, computational complexity, and skill gaps persist. The chapter discusses the need for scalable, transferable, and policy-supported AI solutions for water management in both developed and developing regions, emphasizing the adoption of explainable AI.
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