Application of a Combination Model Based on Wavelet Transform and KPLS-ARMA for Urban Annual Water Demand Forecasting

Abstract

A combination of models including wavelet transform and kernel partial least squares-autoregressive moving average (KPLS-ARMA) is proposed to explore the nonstationarity of the urban annual water demand series, the nonlinear relationships between water demand series and its determinants, and the high correlations among those determinants, based on which a novel forecast model is proposed for urban annual water demand. First, by Mallat algorithm, a nonstationary urban annual water demand series is decomposed and reconstructed into one low-frequency component and one or several high-frequency components. Following that, the kernel partial least squares (KPLS) model is applied to simulating the low-frequency component. An autoregressive moving average (ARMA) model is constructed for each of the high-frequency components. The combined models are applied to understanding the nonstationarity and forecasting the annual water demand of Dalian City. The results are then compared with those from other several methods. It is shown that the proposed method, which combines advanced statistical tools (such as wavelet transform and artificial intelligence) and traditional statistical models, provides the most accurate forecast of urban annual water demand in the city.