Publication Highlight: Predicting and understanding residential water use with interpretable machine learning

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In a recently published Environmental Research Letters article, Predicting and understanding residential water use with interpretable machine learning, authors Dr. Benjamin Rachunok, Aniket Verma, and Dr. Sarah Fletcher apply interpretable machine learning methods to examine how different environmental, demographic, physical housing, and utility policy factors drive residential water use. Their findings suggest that variables across these four areas are useful for predicting residential water use, with environmental and policy factors being the two most important driving factors. Their work provides new insights into the complex drivers of residential water use and can help inform urban water management decisions.

Read the full article linked below!