Mid-Long Term Drought Prediction Based on BVMD-Attention-GRU
Drought is a phenomenon caused by long-term water shortage.Early detection and prediction of drought are crucial for scientific drought prevention and control.To address this,a drought index prediction method based on var-iational modal decomposition algorithm(VMD)and gated recurrent unit(GRU)with attention mechanism is proposed.Firstly,the butterfly optimization algorithm(BOA)is used to optimize the parameters of VMD,which decomposes the standardized precipitation evapotranspiration index(SPEI)data into a set of subsequences with smaller volatility.Then,attention mechanism is introduced into the GRU model to predict each subsequence.Finally,the predicted values of each subsequence are summed up to obtain the SPEI prediction value.The BVMD-Attention-GRU model is applied to predict the SPEI in Urumqi city with a mid-long term forecast period of 6 months,and comparative experiments are conducted u-sing GRU,VMD-GRU,and BVMD-GRU models.The experimental results show that the BVMD-Attention-GRU model has higher prediction accuracy and is suitable for mid-long term drought prediction.
drought predictionbutterfly optimization algorithmvariational modal decompositionattention mecha-nismgated recurrent unit
NETL
NSTL
万方数据
维普
