An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India

Accurate and reliable prediction of the groundwater level variation is significant and essential in water resources management of a basin. The situation is complicated by the fact that the variation of groundwater level is highly nonlinear in nature because of interdependencies and uncertainties in the hydro-geological process. Models such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM) have proved to be effective in modeling virtually any nonlinear function with a greater degree of accuracy. In recent times, combining several techniques to form a hybrid tool to improve the accuracy of prediction has become a common practice for various applications. This integrated method increases the efficiency of the model by combining the unique features of the constituent models to capture different patterns in the data. In the present study, an attempt is made to predict monthly groundwater level fluctuations using integrated wavelet and support vector machine modeling. The discrete wavelet transform with two coefficients (db2 wavelet) is adopted for decomposing the input data into wavelet series. These series are further used as input variables in different combinations for Support Vector Regression (SVR) model to forecast groundwater level fluctuations. The monthly data of precipitation, maximum temperature, mean temperature and groundwater depth for the period 2001–2012 are used as the input variables. The proposed Wavelet-Support Vector Regression (WA-SVR) model is applied to predict the groundwater level variations for three observation wells in the city of Visakhapatnam, India. The performance of the WA-SVR model is compared with SVR, ANN and also with the traditional Auto Regressive Integrated Moving Average (ARIMA) models. Results indicate that WA-SVR model gives better accuracy in predicting groundwater levels in the study area when compared to other models. & 2014 Elsevier B.V. All rights reserved.