Differential Learning for Outliers: A Case Study of Water Demand Prediction

Abstract

Predicting water demands is becoming increasingly critical because of the scarcity of this natural resource. In fact, the subject was the focus of numerous studies by a large number of researchers around the world. Several models have been proposed that are able to predict water demands using both statistical and machine learning techniques. These models have successfully identified features that can impact water demand trends for rural and metropolitan areas. However, while the above models, including recurrent network models proposed by the authors are able to predict normal water demands, most have difficulty estimating potential deviations from the norms. Outliers in water demand can be due to various reasons including high temperatures and voluntary or mandatory consumption restrictions by the water utility companies. Estimating these deviations is necessary, especially for water utility companies with a small service footprint, in order to efficiently plan water distribution. This paper proposes a differential learning model that can help model both over-consumption and under-consumption. The proposed differential model builds on a previously proposed recurrent neural network model that was successfully used to predict water demand in central Indiana.

Forecasting water demand for Istanbul by applying different machine learning algorithms

“…Researchers used inputs such as maximum, average, and minimum temperature, relative humidity, and precipitation for forecasting. Shah et al (2018) proposed a differential model based on recurrent neural network structure that can predict over or under-water consumption. Thus, it tried to minimize the deviations in the water consumption estimation.…”

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

“…Shah et al [42] stated that anticipating urban water demand has been an active field of study for several decades. Also, it becomes increasingly critical due to the scarcity of freshwater resources and an increase in water consumption resulting from socioeconomic and climate factors.…”

Forecasting Urban Water Demand Using Cellular Automata

“…Shah et al [20] proposed a differential learning model based on a neural network to model over-and under-consumption. Although the model did not considerably reduce the forecast error for days with average water demand, it did provide lower and upper limit estimates for water demand for atypical days.…”