Review of machine learning and WEAP models for water allocation under climate change

Abstract

Abstract This paper examines how machine learning (ML) techniques can enhance the Water Evaluation and Planning (WEAP) model for surface water distribution strategies in the context of climate variability. Recent advancements in ML, General Circulation Models (GCMs), satellite data, and climate projections offer new opportunities for improved water resource management. However, methodological challenges remain in integrating these techniques across scientific disciplines and translating theoretical research into practical applications. The paper evaluates the effectiveness of the WEAP model in scenario planning while identifying uncertainties arising from dynamic socio-economic changes and climate variability. It demonstrates how ML enhances WEAP’s capabilities by improving forecasting accuracy, recognising hydrological patterns, and reducing measurement uncertainties. Furthermore, scenario-based modelling, powered by ML, offers sustainable water management solutions tailored to water-stressed regions facing increasing environmental and demand pressures. By synthesising insights from diverse research, this paper offers actionable recommendations for researchers, policymakers, and practitioners seeking to develop resilient water distribution systems in vulnerable regions.