Reservoir Drought Prediction Using Two-Stage SVM

Abstract

The support vector machine (SVM) has been applied to drought prediction and it typically yields good performance on overall accuracy. However, the prediction accuracy of the drought category is much lower than that of the non-drought and severe drought categories. In this study, a two-stage approach was used to improve the SVM to increase the drought prediction accuracy. Four features, (1) reservoir storage, (2) inflows, (3) critical limit of operation rule curves, and (4) the Nth ten-day in a year, were used as input data to predict reservoir drought. We used these features as input data because they are the most commonly kept records in all reservoir offices. Empirical results show that the two-stage SVM outperforms the original SVM and the three other approaches (artificial neural networks, maximum likelihood classifier, Bayes classifier) for drought prediction. Not surprisingly, the longer the prediction time period, the lower the prediction accuracy is. However, the accuracy of predicting conditions within the next 50 days was approximately 85% both in training and testing data set by the two-stage SVM. Drought prediction provides information for reservoir operation and decision making in terms of water allocation and water quality issues. The result shows the benefit of a two-stage approach of SVM for drought prediction, as the accuracy of drought prediction increased quite substantially.