Drought prediction model for the Xilingol grassland based on BO-CNN-LSTM
[Objective]Reliable and effective monitoring can mitigate the impact of drought disasters on socio-economic development and natural ecosystems. This study adopted the BO-CNN-LSTM coupled neural network as adrought prediction model.[Method]First,the memory function of long short-term memory (LSTM) was inte-grated into the fully connected layer of the convolutional neural network (CNN). Second,to determine the optimal hyperparameters for LSTM,the probability surrogate model and acquisition function from the Bayesian optimization (BO) algorithm wereintroduced. Finally,a BO-CNN-LSTM coupled neural network model was constructed to predict the drought conditions in the Xilingol grassland.[Result](1) The prediction accuracy of the BO-CNN-LSTM model increased with the time scale,withthe highest prediction accuracy observed under the 12-month scale for the Standardized Precipitation-Evapotranspiration Index (SPEI). The determination coefficient R2 of SPEI-12 for each site exceeded 98％.(2) Compared to the simulation results of the LSTM model for SPEI-12,the proposed model exhibited higher fitting accuracy,showinga relative improvement in R2 of[4.63％,8.67％]. The order of magnitude of mean squared error (MSE) at each site had decreased from 10-2 to 10-3.(3) Using the model to predict the spatial distribution of drought in the Xilingol grassland for 2023. indicated that the region as a whole was experi-encing drought.Especially,the Dongwuzhumuqin Banner area was classified as experiencing moderate drought,while other areas were classified as severe drought.[Conclusion]The results demonstrate that the BO-CNN-LSTM model has high computational accuracy,making it particularly suitable for predicting SPEI-12.Therefore,the meth-ods in this study can be effectively applied to drought prediction on an annual time scale.
万方数据
维普
