A Review of Deep Learning Applications for Sustainable Water Resource Management

Abstract

Deep learning (DL) techniques and algorithms have the capacity to significantly impact world economies, ecosystems, and communities. DL technologies have been utilized in the development and administration of urban structures. However, there exists a dearth of literature reviewing the present level of these applications and exploring potential directions in which DL can address water challenges. This study aims to review demand projections, leakage detection and localization, drainage defect and blockage, cyber security and wealth surveillance, wastewater recycling and management, water safety prediction, rainfall conversation, and irrigation regulation. The application of DL techniques is currently in its early stages. Most studies have adopted standard networks, simulated information, and experimental or prototype settings to evaluate the efficacy of DL approaches. However, there have been no reported instances of practical adoption. Compared to other reviewed problems, leakage detection is currently being implemented practically in daily operations and handling of water facilities. The major challenges for the practical deployment of DL in water management include algorithmic development, multi-agent platforms, virtual clones, data quality and availability, security, context-aware data analysis, and training efficiency. We validate our study by using several case studies that employ DL for water treatment. Prospective exploration and deployment of DL systems are anticipated to advance water systems toward increased cognition and flexibility. This research aims to encourage further research and development in utilizing DL for feasible water usage and digitalization of the global water sector.