Predicting the Impact of Climate Conditions on the Economic Production of Iranian Provinces with The Approach of Random Forest Algorithm

The importance of climate as one of the human biological facts in the field of macro-economic and social issues has never been considered as much as today. Investigating the economic effects of climate change requires detailed analyzes at the national and local levels. Although there are several global studies that examine the economic impacts of climate change, so far limited studies have been conducted at local levels within countries, especially in the case of Iran. In this article, an attempt has been made to make a comprehensive analysis of the influence of the economic production of the country's provinces on climate change by using the new data set of the weather conditions of the country's provinces in the period from 2000 to 2020 through the random forest algorithm of machine learning subsets. Submitted. The results show that temperature and precipitation affect production in all provinces of the country. The forecast of production changes in Kerman, Semnan, North Khorasan, Ilam, Qazvin and Kohgiluyeh and Boyar Ahmad provinces is presented with more effectiveness than other provinces, and also the influence of precipitation compared to temperature is presented with higher importance in the model, while the importance of the influence of temperature on production in the warm months of the year are predicted to be less than the cold months of the year