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.

ML-based regionalization of climate variables to forecast seasonal precipitation for water resources management

“…With the extracted features for predictors, the GLM and SVM models were applied to forecast the target ASP. The GLM model is a linear model while the SVM model was further tested to check if any performance improvement could be made with more complex models (Chiang and Tsai 2013, Hipni et al 2013, Mokhtarzad et al 2017. For the GLM model, the Gamma distribution was applied, since the employed precipitation data has been known to follow this distribution with a nonnegative domain (Banik et al 2002, Ferraris et al 2003, Hanson and Vogel 2008, Moron et al 2008, Zheng and Katz 2008, Lee 2018.…”

Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique

“…Support Vector Regression (SVR) is a popular ML approach in hydrologic forecasting. It has been used in many studies for drought prediction and papers have shown that SVR is a promising tool in drought prediction [19][20][21][22][23][24]. In addition to the use of ML models, researchers also started to produce hybrid models by combining with other techniques, such as ensemble and pre-processing technique.…”