Drought Prediction Based on Feature-Based Transfer Learning and Time Series Imaging

Abstract

Drought is an extreme climate phenomenon that has a great impact on the economy, tourism, agriculture, and water resources. Drought prediction can provide an early warning of the occurrence of drought and reduce losses. In this article, the standard precipitation evapotranspiration index (SPEI) on four time scales: SPEI-3, SPEI-6, SPEI-9, and SPEI-12 are used to measure and predict drought. Unlike the general methods of directly modeling the SPEI index, time-series imaging and feature-based transfer learning are used to extract the features of the SPEI sequence and use the extracted features for prediction. First, we use Gramian Angular Summation/Difference Field (GASF/GADF), Markov Transition Field (MTF), and Recurrence Plot (RP) as the time series imaging techniques to encode SPEI sequences into images. Secondly, we utilize imaging data sets and convolutional neural networks (CNNs) such as residual network (ResNet) and VGG to train the feature extraction network. Finally, the following four regressors: Random Forest (RF), Long and Short-Term Memory network (LSTM), Wavelet Neural Network (WNN), Support Vector Regression (SVR) are used to model the extracted features and drought prediction. To verify the effectiveness of the method proposed in this article, we use the SPEI of four time scales at eight stations in the Haihe River Basin for prediction. Compared with the existing methods, the prediction results of different time scales and stations are improved. For example, after feature extraction, LSTM can reach MAPE = 0.5400, SMAPE = 0.4452, MAE = 0.2150, MSE = 0.0853 and $R^{2} = 0.8960$ in the SPEI-12 prediction of the Beijing site, and other results show that the proposed method is not sensitive to the time scale of drought prediction.