Reservoir Drought Prediction Using Support Vector Machines

Abstract

In Taiwan, even though the average annual rainfall is up to 2500 mm, water shortage during the dry season happens sometimes. Especially in recent years, water shortage has seriously affected the agriculture, industry, commerce, and even the essential daily water use. Under the threat of climate change in the future, efficient use of water resources becomes even more challenging. For a comparative study, support vector machine (SVM) and other three models (artificial neural networks, maximum likelihood classifier, Bayesian classifier) were established to predict reservoir drought status in next 10-90 days in Tsengwen Reservoir. (The ten-days time interval was applied in this study as it is the conventional time unit for reservoir operation.) Four features (which are easily obtainable in most reservoir offices), including reservoir storage capacity, inflows, critical limit of operation rule curves, and the number of ten-days in a year, were used as input data to predict drought. The records of years from 1975 to 1999 were selected as training data, and those of years from 2000 to 2010 were selected as testing data. The empirical results showed that SVM outperforms the other three approaches for drought prediction. Unsurprisingly the longer the prediction time period is, the lower the prediction accuracy is. However, the accuracy of predicting next 50 days is about 85% both in training and testing data set by SVM. As a result, we believe that the SVM model has high potential for predicting reservoir drought due to its high prediction accuracy and simple input data.

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