Innovative Machine Learning Applications in Non-Revenue Water Management: Challenges and Future Solution

Abstract

The escalating global concerns surrounding Non-Revenue Water (NRW) necessitate a paradigm shift in water management strategies, and innovative machine learning (ML) applications emerge as a transformative solution. This paper investigates the intersection of ML and NRW management, recognizing the pressing need to curb water losses due to leaks, theft, and inaccuracies. As water utilities grapple with the economic and environmental repercussions of NRW, this paper explores the potential of ML algorithms, such as predictive analytics to revolutionize traditional approaches. The discussion encompasses the intricate landscape of challenges, including data quality issues, model interpretability, and the inherent complexity of implementation. Recognizing the multidisciplinary nature of these challenges, the journal emphasizes the collaborative efforts required to harmonize technological innovation with practical implementation. As the world confronts the imperative to optimize water resources, this paper posits that innovative ML applications present a pivotal opportunity to not only enhance the accuracy and efficiency of NRW management but also to foster a more sustainable and resilient water infrastructure. Through a comprehensive grasp of challenges and a proactive pursuit of remedies, stakeholders can establish sustainable, resilient, and equitable water management systems. This paper acts as a valuable reference, providing a detailed discussion encompassing Europe, China, Japan, South Korea, and specific states within Malaysia, with a specific focus on non-revenue water (NRW) systems.