Abstract

Increasing population and the rising air temperatures are known as factors that cause water depletion in the watersheds. Therefore, it is important to accurately predict the future ratios of tap water consumers using the same watershed to the population living in the specified area, to produce better water policies and to take the necessary measures. Predictions can be made by a growth curve model (GCM). Parameter estimations of the GCM are usually based on the ordinary least square (OLS) estimator. However, the outlier presence affects the estimations and the predictions, which are obtained by using the estimated model. The present article attempts to construct first- and third-order GCMs with robust least median square (LMS) and M estimators to make short-term predictions of ratios of tap water consumers. According to the findings, parameter estimations of the models, the outliers, and the predictions vary with respect to the estimators. The M estimator for short-term predictions is suggested for use, due to its robustness against outlier points.

Highlights

  • Water has vital importance for the survival of living beings

  • Regarding the estimated first-order growth curve model (GCM) in Figure 1a, it is observed that the GCMs based on the ordinary least square (OLS), least median square (LMS), M estimators are different

  • Knowing that the OLS estimator is being influenced by the outlier points, it is better to evaluate the predictions obtained from LMS and M estimators due to their robustness to outlier points [18,19,20]

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Summary

Introduction

Water has vital importance for the survival of living beings. It is the basic element in terms of maintaining life in nature and human activities. A tiny fraction of the water that forms the large part of our planet we live consists of healthy drinkable water. This water is renewed by nature’s solar-powered water cycle. With the evaporation fueled by the sun’s energy, water vapor is carried to the atmosphere. Of this evaporation, 86% occurs from the sea and 14% from land [1]

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