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.