Fuzzy decision-tree regression model and its application to measure some climate change factors

In this paper, we considered a new study that examines the topic of climate change based on data from two important variables: temperature and wind speed. The study aims to employ a decision-making method based on fuzzy logic to overcome the issue of ambiguity and uncertainty. Our proposed idea in this paper was to construct an appropriate analytical framework for the phenomenon, with the aim of arriving at a more accurate decision to overcome the risks of this phenomenon and take appropriate precautions in the near and distant future to deal with this natural emergency that is increasing over time. We discussed how to implement the GUIDE regression tree algorithm as a main tool in analyzing fuzzy sets using the Triangular Membership Function to fuzzify the data to obtain more accurate partial fuzzy sets for description in the analysis of chi-square tables to make a decision using a suitable hypothesis for this purpose. The proposed method was applied to a sample size of 425 daily observations in Dhi-Qar Governorate, Iraq, for the period from December 2024 to February 2025. We used a special code in R programming for the purpose of analysis and obtaining results. Through analyzing the results, we found that two variables (temperature and wind speed) have a fundamental influence on the speed of climate change.