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Predicting the impacts of climate change on the production of the agricultural sector and the Iranian economy using artificial intelligence models | ||
| Iran Agricultural Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 خرداد 1405 اصل مقاله (1.84 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2026.54699.1725 | ||
| نویسندگان | ||
| Zakaria Farajzadeh* ؛ Alireza Keshavarz | ||
| Department of Agricultural Economics, School of Agriculture, Shiraz University, Shiraz, I. R. Iran | ||
| چکیده | ||
| Climate change is especially severe in developing countries such as Iran, which have a low capacity to address this issue. A variety of complex models and methodologies which are reliant on extensive datasets have been employed to evaluate the economic and environmental impacts. In other words, alongside the primary variable indicating climate change, specifically temperature anomalies, numerous economic and environmental variables and datasets must be incorporated. This study was undertaken with the objective of evaluating the viability of discovering alternative models through the application of machine learning (ML) and artificial intelligence (AI)-based frameworks. The data utilized is derived from the related literature for the period 2021-2060, which outlined the production effects of climate change. The temperature change scenarios encompass SSP2-4.5, SSP3-7.0, and SSP5-8.5. A wide array of AI-based models was employed. The study's findings indicated that by utilizing the temperature anomaly variable, it is feasible to forecast the decline in Iran's gross domestic product (GDP) with a prediction error of fewer than 5% through multilayer perceptron (MLP) and random forest (RF) models, as well as their combinations. For agricultural subsectors, using the hybrid model, this error was assessed to be below 3%. Concerning agricultural products, it was found that the combined models are capable of predicting accurately the extent of loss even at higher levels of temperature anomaly. The high sensitivity of agricultural activities to climate change, coupled with the complex and multifaceted nature of climate change itself, has made it essential to use large datasets and sophisticated models. In this regard, ML and AI offer a promising, though not flawless, solution. Given the highly favorable accuracy and rapid access to reliable predictions afforded by AI-based models, the implementation of these models, particularly the combinations of MLP and RF, is recommended. | ||
| کلیدواژهها | ||
| Temperature anomaly؛ Output loss؛ Machine learning | ||
| مراجع | ||
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