Prediction of DEDI index for meteorological drought with the VMD-CBiLSTM hybrid model

Abstract

Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance.