Improving the Interpretation of Data-Driven Water Consumption Models via the Use of Social Norms

Abstract

Water is essential to improving social equity, promoting just economic development and protecting the function of the Earth system. It is therefore important to have access to credible models of water consumption, so as to ensure that water utilities can adequately supply water to meet the growing demand. Within the literature, there are a variety of models, but often these models evaluate the water consumption at aggregate scales (e.g., city or regional), thus overlooking intra-city differences. Conversely, the models that evaluate intra-city differences tend to rely heavily on one or two sources of quantitative data (e.g., climate variables or demographics), potentially missing key cultural aspects that may act as confounding factors in quantitative models. Here, we present a novel mixed-methods approach to predict intra-city residential water consumption patterns by integrating climate and demographic data, and by incorporating social norm data to aid the interpretation of model results. Using Indianapolis, Indiana as a test case, we show the value in adopting a more integrative approach to modeling residential water consumption. In particular, we leverage qualitative interview data to interpret the results from a predictive model based on a state-of-the-art machine learning algorithm. This integrative approach provides community-specific interpretations of model results that would otherwise not be observed by considering demographics alone. Ultimately, the results demonstrate the value and importance of such approaches when working on complex problems.