Machine Learning for Water Resources Management

Abstract

Water resources management challenges are multiple and complex, and human force, as the main driver of environmental change, has been increasing the need for tailored (and faster) responses to them over the past decades. Despite our increasing technical knowledge on how to tackle these issues, which are mainly related to water quantity, quality and access, unprecedented change in climate and landscapes will require a better understanding of the interactions between water and society. This thesis is concerned with the challenging task of applying machine learning techniques to extract knowledge from hydrological, socioeconomic and climate data and tackle some of the water management issues associated with water quantity and quality. Specifically, it addresses (i) the drivers of water demand in different temporal and spatial scales; (ii) the implications of price-based demand-side measures and how media coverage and public interest on extreme events can affect consumption habits; (iii) the long-term water availability and supply under climate variability, and (iv) some of the effects of environmental change on water quality. We learn that climate variability and change might affect not only hydrological responses but also consumption habits and water supply expansion strategies. Also, we make valuable findings on the drivers of water demand and quality, which can support utilities in their long-term planning.