Prediction of Egional Temperature Change Trend Based on LSTM Algorithm

Abstract

In order to better predict the trend of temperature in future regions, a time recurrent neural network algorithm LSTM is proposed to predict regional temperature trends. This paper obtains temperature changes in Alberta, Quebec, and Saskatchewan, Canada. Based on the average temperature timing characteristics of each province, LSTM (long short-term memory) is used to analyze the provinces of Canada. Temperature and time trends of temperature and modelling, predicting temperature changes in future Canadian provinces; The results show that after the above model predicts the temperature change trend for the next three years, the predicted temperature change trend is almost consistent with the existing data, and the prediction accuracy is also relatively high. Therefore, the LSTM algorithm based on this paper can be applied to the prediction of regional temperature trends, and the prediction results and accuracy are very good, which has certain value and significance for real life.