Drought Level Prediction Based on Meteorological Data and Deep Learning

Drought has been a global concern and an effective prediction method is needed. Meteorological data are seen as an efficient and economic approach. Challenges arise with the large volume and high nonlinearity between meteorological variables and the drought level. In this study, deep learning is proposed as an effective solution for drought level prediction as multivariate time series classification. The synthetic minority oversampling technique is further adopted to alleviate the class imbalance problem and improve the classification performance. Experimental results on an open dataset named DroughtED demonstrate the effectiveness of the proposed deep learning method.