Machine Learning-Based Temperature Forecasting for Sustainable Climate Change Adaptation and Mitigation

Abstract

In this study, temperature estimation was achieved by utilizing artificial neural network (ANN) and machine learning models (linear model, support vector machine, K-nearest neighbor, random forest) to assist with sustainable environmental planning and climate change adaptation solutions. The research compared monthly humidity, wind speed, precipitation, and temperature data of the Istanbul province from 1950 to 2023. Estimates with 96% accuracy were achieved with the ANN model, and amongst the machine learning models, the random forest (RF) model demonstrated the highest performance. Generalization capability of the models was enhanced by the k-fold cross-validation method. The analysis found input variables (humidity, wind, precipitation) to be negatively associated with temperature. The current results show that the application of artificial intelligence/machine learning techniques is a useful instrument in the context of sustainable climate monitoring and temperature estimation. This study achieves sustainability targets through certain reliable methodologies for climate change evaluation, sustainable energy design, and agricultural adaptation plans. The methodology is transferable to other regional climate analyses and has the potential to underpin evidence-based, decision making for sustainable development and climate resilience.