Nature’s Guidance: Employing Bio-inspired Algorithm and Data-Driven Model for Simulating Monthly Maximum and Average Temperature Time Series in the Middle Black Sea Region of Türkiye

Abstract

This study compares the performance of various models in predicting monthly maximum and average temperatures across three distinct regions: Samsun, Amasya, and Çorum. The evaluated models include Artificial Neural Network (ANN), Shuffled Frog Leaping Algorithm coupled with ANN (SFLA-ANN), Firefly Algorithm coupled with ANN (FFA-ANN), and Genetic Algorithm coupled with ANN (GA-ANN). In setting up the models, the dataset was divided into 70% for training and 30% for testing, and the outputs of the models were evaluated using various graphical and statistical indicators. The model with the smallest root mean square error (RMSE) value was selected for the maximum and average temperature predictions. Accordingly, for maximum and average temperature predictions, SFLA-ANN (RMSE of 2.93) and GA-ANN (RMSE of 3.55) in Samsun, GA-ANN (RMSE of 2.91) and GA-ANN (RMSE of 2.50) in Amasya and GA-ANN (RMSE of 2.97) and GA-ANN (RMSE of 2.50) in Çorum performed better than the other models, respectively. In addition, for the maximum temperature prediction with the highest accuracy, the R² value of the SFLA-ANN model in Samsun was 0.89. In contrast, the R² values of the GA-ANN model in Amasya and Çorum were determined as 0.91 and 0.91, respectively. Similarly, it was observed that the R² values of the GA-ANN model for the average temperature prediction with the highest accuracy at Samsun, Amasya and Çorum stations were 0.78, 0.92 and 0.92, respectively. Overall, the GA-ANN consistently demonstrated superior performance in predicting both maximum and average temperatures across all three regions, as evidenced by its consistently low RMSE values. These findings provide valuable insights into selecting effective models for temperature prediction tasks in different geographical regions.