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Wavelet-Coupled Machine Learning Methods For Drought Forecast Utilizing Hybrid Meteorological And Remotely-Sensed Data, R. Tan, Marek Perkowski
Wavelet-Coupled Machine Learning Methods For Drought Forecast Utilizing Hybrid Meteorological And Remotely-Sensed Data, R. Tan, Marek Perkowski
Electrical and Computer Engineering Faculty Publications and Presentations
In this study, a statistical drought early warning method is proposed using novel machine learning algorithms, with the inclusion of multiple drought-related attributes from precipitation, satellite-derived land cover vegetation indices, and surface discharge. The forecast is made for the long-term hydrological drought in the region of Central Valley, California. The wavelet transform analysis is employed in combination with support vector regression and artificial neural network algorithms for improving the drought prediction effectiveness. The performance of the drought prediction is evaluated using three statistical metrics: Coefficient of Determination (R2 ), Root-Mean-Square Error (RMSE), and Mean-Absolute-Error (MAE). The results clearly indicate that …