Sequential prediction of daily groundwater levels by a neural network model based on weather forecasts

Abstract

This paper investigates the implementation of an Artificial Neural Network (ANN) model for sequential prediction of daily groundwater levels based on precipitation forecasts. The basic principle of the ANN-based procedure consists of relating previous daily groundwater levels and daily precipitation forecasts in order to predict daily groundwater levels up to seven days ahead. The daily precipitation values up to one week ahead are assumed to be deterministic since meteorological short-range forecasts are generally available. The methodology is applied to the groundwater system of Matsuyama City, Japan. Insufficiency of water is a periodical problem in this city and thus accurate predictions of groundwater levels are very important to improve the water resources management in the region. The excellent accuracy obtained by the ANN model indicates that it is very efficient for the multi-step-ahead prediction of daily groundwater levels. As conclusion, this methodology may provide trustworthy data for the application of models to the sustainable management of Matsuyama s groundwater system.