Maximum temperature forecasting using deep learning algorithm by hyperparameter optimization

\nThe prediction of the daily temperature, an important meteorological variable, has been a topic of interest among researchers currently. The adverse impact of climate change on the livelihood of human beings makes it a contentious issue, hence the importance of accurate temperature predictions. In this paper, a global temperature change prediction model that adopts deep learning (DL) algorithms was presented which preprocess the Extreme-Weather Temperature Prediction Time Series Data by removing outliers using the standard deviation and normalizing the data. Statistical feature techniques are used for the extraction of characteristics, and forecasting is conducted using the Deep Belief Network (DBN) classifier. The proposed Egret Swarm Optimisation (ESO) method was used in training the multilayer perceptron (MLP) layer of the DBN. The success of the forecast is evaluated using mean absolute error (MAE), squared coefficient of correlation (R2), and root mean square error (RMSE). The results prove that the proposed model is better than as it has the lowest MAE (0.827), RMSE (0.892), the highest correlation (0.988), and the lowest Mean Absolute Relative Error (MARE) (0.126), showing a good linear relationship between the predicted and observed values, and low relative error (MARE). This makes it a significant advancement in temperature prediction.\n