Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards - Allen-Dumas, Melissa R.; Xu, Haowen; Kurte, Kuldeep R.; Rastogi, Deeksha (Frontiers in Water, 2021)
Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine-learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.