摘要
Abstract
Desertification is a global environmental issue that affects many countries and regions.Accurate prediction of desert area is essential to develop effective desertification control strategies.In this paper,different models are used to predict the desert area to find out a model with high prediction accuracy and excellent model performance.The desert in the eastern part of Ruoqiang County,Xinjiang is taken as the research object and the models of ARIMA(autoregressive integrated moving average),RNN(recurrent neural network),LSTM(long short-term memory),GRU(gated recurrent unit),ARIMA-RNN,ARIMA-LSTM and ARIMA-GRU are used to predict the desert area.The model performance is assessed with mean square error(MSE),root-mean-square error(RMSE)and mean absolute error(MAE).The experimental results show that the model ARIMA is found to have the lowest prediction accuracy and the worst performance,and the deep learning model has the highest prediction accuracy of about 96.74%.The prediction accuracy of the combined model can be as low as about 93.08%,among which the highest prediction accuracy of the combined model ARIMA-GRU reaches about 97.46%.The experiments show that the deep learning model has high prediction accuracy and good performance in desert area prediction.The combined model can improve the accuracy and stability of desert area prediction,and can avoid the limitations and risks of single model prediction.关键词
沙漠化/深度学习/组合模型/沙漠面积/模型预测/ARIMAKey words
desertification/deep learning/combined model/desert area/model prediction/ARIMA分类
信息技术与安全科学
