Interpretable Machine Learning for Predicting Climate Change Effect on Agricultural Land Suitability in Eurasia
Authors-
Keyword-Climate Change, Agricultural Land Suitability, Interpretable Machine Learning, Explainable AI, SHAP, Random Forest, Gradient Boosting, Eurasia, Spatial Analysisltural policy and climate adaptation strategies.
Temperature and precipitation patterns are changing quickly due to climate change, which pre-sents significant obstacles to the sustainability of agriculture throughout Eurasia. Effective long-term planning requires accurate forecasting of changes in the suitability of agricultural land. High precision and interpretability are frequently lacking in conventional statistical models. An inter-pretable machine learning method for evaluating the effects of climate change on the usability of agricultural land is presented in this study. The input features include a variety of climate indica-tors, soil characteristics, and land-use factors. Prediction is the goal of machine learning models like Random Forest and Gradient Boosting. Standard accuracy and error measures are used to assess the model's performance. Interpretability strategies like SHAP are used to improve trans-parency. The relative significance of environmental and climatic elements is revealed by these techniques. To find geographical differences, spatial analysis is done.
Doi-[https://doi.org/10.5281/zenodo.21735896]