Abstract
The United States is one of the largest per capita water withdrawers in the world, and certain parts of it, especially the western region, have long experienced water scarcity. Historically, the U.S. relied on large water infrastructure investments and planning to solve its water scarcity problems. These large-scale investments as well as water planning activities rely on water forecast studies conducted by water managing agencies. These forecasts, while key to the sustainable management of water, are usually done using historical growth extrapolation, conventional econometric approaches, or legacy software packages and often do not utilize methods common in the field of statistical learning. The objective of this study is to illustrate the extent to which forecast outcomes for commercial, institutional and industrial water use may be improved with a relatively simple adjustment to forecast model selection. To do so, we estimate over 352 thousand regression models with retailer level panel data from the largest utility in the U.S., featuring a rich set of variables to model commercial, institutional, and industrial water use in Southern California. Out-of-sample forecasting performances of those models that rank within the top 5% based on various in- and out-of-sample goodness-of-fit criteria were compared. We demonstrate that models with the best in-sample fit yeild, on average, larger forecast errors for out-of-sample forecast exercises and are subject to a significant degree of variation in forecasts. We find that out-of-sample forecast error and the variability in the forecast values can be reduced by an order of magnitude with a relatively straightforward change in the model selection criteria even when the forecast modelers do not have access to “big data” or utilize state-of-the-art machine learning techniques.
Highlights
The United States (U.S.) is one of the largest per capita water withdrawers in the world [1]
The historic mismatch between the location of water supply and demand has shaped water infrasTtrhuecthuirsetoirnivcemstimsmenattcdhecbiestiownesenantdhepllaoncantiinogn aocftiwviatiteesr isnupthpelyUannitdeddeSmtaatensd, ohnaes oshf athpeedlawrgaetsetr winaftrearsutrsuecrtsuirnetihnevwesotmrlden—t bdoetchisiinontesramnsdopf ltaontanlinagndacpteivrictiaepsitian wthaeteUr nuistee.dTShteasteesd, eocniseioonfsthaerelaorfgteenst guided by water forecast studies from utilities that rely on conventional econometric methods and/or black box software packages when generating CII water use forecasts
Splitting a dataset into a training set and validation set is a prominent idea in the field of statistical learning
Summary
The United States (U.S.) is one of the largest per capita water withdrawers in the world [1]. It has a large water supply overall; water scarcity is still a challenge as water is not present where, when, and in the form it is needed. California, by far the largest U.S state both by population [7] and by economic activity measured using gross domestic product (GDP) [8], requires all water utilities serving more than 3000 users to formulate an Urban Water Management Plan (UWMP) every five years [9].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have