Assessing Climate Change Impacts on Agricultural Regional Gross Products in the Urmia Watershed: A Data Mining Approach

This study assesses the impacts of climate change on agriculture in the Urmia Basin using the Random Forest algorithm to project future scenarios under SSP1-2.6 and SSP5-8.5 climate pathways. Historical data indicates an average annual precipitation of 291 mm, an air temperature of 11.29°C, and evapotranspiration of 1086 mm. Projections for 2025-2035 suggest a warming trend, with temperatures rising to 12.02°C under SSP5-8.5, accompanied by reduced precipitation and increased evapotranspiration. These changes are anticipated to negatively affect agricultural productivity, with the SSP5-8.5 scenario projecting a lower Gross Regional Product (GRP) due to decreased precipitation, higher evapotranspiration, and reduced area of cultivation. The analysis of feature importance, utilizing LULC MODIS data to extract cultivation areas, revealed that precipitation and the area of cultivation are the most significant factors influencing the model's predictions. The model demonstrated acceptable predictive accuracy, with a Kling-Gupta Efficiency (KGE) of 0.54 and a Root Mean Square Error (RMSE) of 0.31. Additionally, declining agricultural productivity may drive increased urban expansion and strain on urban infrastructure, underscoring the need for integrated adaptation and development strategies.

Assessing Climate Change Impacts on Agricultural Regional Gross Products in the Urmia Watershed: A Data Mining Approach Keywords:

Machine learning , climate change , Agriculture , Urmia Lake Watershed