A computational intelligence framework for urban water security: integrating digital twins and hybrid AI for arid climates

Gazder, Uneb, Islam, Md. Kamrul, Dahman, Nidal Abdul Rahim and Mourshed, Monjur ORCID: https://orcid.org/0000-0001-8347-1366 2026. A computational intelligence framework for urban water security: integrating digital twins and hybrid AI for arid climates. Mirrashid, M., Jahed Armaghani, D. and Azizi, A., eds. Optimization and Adaptive Control Algorithms. Emerging Trends in Mechatronics, Springer, pp. 25-44. (10.1007/978-981-95-8387-4_2)

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Abstract

A significant issue facing the Gulf Cooperation Council (GCC) nations is water scarcity. The causes are rapid urbanization, dwindling freshwater supplies, and arid conditions. The water management system for Bahrain suggested by this study incorporates desalination, stormwater harvesting, aqua harvesting, and permeable pavements. Water scarcity is a major challenge for the Gulf Cooperation Council (GCC) countries. It is caused by arid climates, limited freshwater resources, and rapid urban growth. This study proposes a water management framework for Bahrain that combines permeable pavements, stormwater harvesting, aqua harvesting, and desalination. This approach would integrate digital twin technology for real-time water system modeling with hybrid machine learning models. The study is based on a review of published research, selected case studies, and an examination of existing technologies in the region. The development of the framework initially emphasized both qualitative and quantitative aspects of water provision. Quantum computing was suggested as a tool to enhance the supply of safe water. The qualitative part of the framework focuses on optimizing the location of sensors in pipelines for monitoring water quality. In the quantitative part, the framework employs graph neural networks (GNNs) for network optimization, transformer models for demand forecasting, and reinforcement learning for adaptive control systems. For optimization challenges, the framework utilizes advanced algorithms, including metaheuristic approaches for location optimization, and stochastic optimization methods for handling uncertainty. The framework is designed to address several challenges linked to water security and aims to address key challenges in water security. An artificial intelligence-based digital twin model can support this work by handling microlevel models that involve many variables. An AI-based digital twin model supports this framework by handling complex, multivariable micro- level models. The development and implementation of such a framework are considered vital for Bahrain and other arid regions, including neighboring countries like Saudi Arabia.

Item Type:	Book Section
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Springer
ISBN:	9789819583874
Date of First Compliant Deposit:	16 August 2026
Last Modified:	17 Aug 2026 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/189003

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