Prediction of Meteorological Drought in Xinjiang at Multiple Temporal Scales Based on GWO-SA-ConvBiLSTM

Abstract

Drought is one of the most serious climatic disasters affecting human society. Effective drought prediction can provide a reliable basis for the formulation of anti-drought measures. According to drought characteristics, we construct a multi-time scale GWO-SA-ConvBiLSTM network. In this model, we combine Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), and add the self-attention mechanism (SA). On this basis, the grey Wolf optimizer(GWO) is added to make the model choose the optimal hyperparameter faster. We selected Atel region of Xinjiang as the research object, sorted out the meteorological data of 5 meteorological stations in the study area from 1960 to 2018, and imported their SPEI values of 1, 3, 6, 12 and 24 months into the model for training. Compared with other models, our model has better performance in the scenario of drought prediction.