Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model
It can be inferred that XGBoost is applicable for streamflow forecasting, and in general, performs better than SVM; the cluster analysis-based modular model is helpful in improving accuracy and capturing the complicated patterns of hydrological process.
Abstract
The establishment of an accurate and reliable forecasting model is important for water resource planning and management. In this study, we developed a hybrid model (namely GMM-XGBoost), coupling extreme gradient boosting (XGBoost) with Gaussian mixture model (GMM), for monthly streamflow forecasting. The proposed model is based on the principle of modular model, where a complex problem is divided into several simple ones. GMM was applied to cluster streamflow into several groups, using the features selected by a tree-based method. Then, each group was used to fit several single XGBoosts. And the prediction is a weighted average of the single models. Monthly streamflow data at Cuntan and Hankou stations on Yangtze River Basin were used to evaluate the performance of the proposed model. To compare the forecasting efficiency, support vector machine (SVM) and standalone XGBoost were selected as the benchmark models. The results indicated that although all three models yielded quite good performance on one-month ahead forecasting with high Nash-Sutclitte efficiency coefficient (NSE) and low root mean squared error (RMSE), GMM-XGBoost provided the best accuracy with significant improvement of forecasting accuracy. It can be inferred from the results that (1) XGBoost is applicable for streamflow forecasting, and in general, performs better than SVM; (2) the cluster analysis-based modular model is helpful in improving accuracy and capturing the complicated patterns of hydrological process; (3) the proposed GMM-XGBoost model is a superior alternative, which can provide accurate and reliable predictions for optimal water resources management.