Spatio-temporal forecasting model of water balance variables in the San Diego aquifer, Venezuela

Abstract

In this paper, a spatio-temporal forecasting model of water balance variables in the San Diego aquifer, Venezuela is proposed combining tools of GIS as the geostatistical analyst tool to make prediction of variables using statistical spatial prediction models based on the Ordinary Krigging followed by the application of forecasting models including those as: linear trend, quadratic trend, exponential trend, moving average, simple exponential smoothing, Brown’s linear exponential smoothing, quadratic exponential smoothing and autoregressive integrated moving average (ARIMA). The spatio-temporal forecasting models of water balance variables in the San Diego aquifer have been calibrated and validated showing a successful adjustment to the water balance variables as the following five variables: 1) precipitation, 2) evapotranspiration, 3) pumping flow, 4) infiltration and 5) volume stored. In the calibration stage, the statistical spatial prediction model selected has been J-Bessel and the forecasting model selected has been Brown's quadratic exp. smoothing with constant alpha.  In the validation stage, the correlation coefficient has taken values upper to 0.98 and the determination coefficient upper to 0.96 confirming that the method used to generate the spatio-temporal forecasting model to achieve good predictions to the water balance variables.