Climate change is intensifying environmental hazards, particularly in semi-arid regions where water scarcity, temperature extremes, and rainfall variability pose severe risks to ecosystems and livelihoods. Traditional climate impact assessment methods face limitations in handling high-dimensional data and complex, non-linear relationships among climatic and environmental variables. This study applies Artificial Intelligence (AI) models including Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) — to assess climate change impacts in Ahilyanagar District, a representative semi-arid region in Maharashtra, India. Utilizing long-term climatic datasets, remote sensing imagery, and geospatial analysis, the paper quantifies trends in temperature, rainfall, drought occurrence, vegetation stress, and land use dynamics. The results demonstrate that AI models significantly enhance predictive accuracy and spatial characterization, revealing increasing temperature trends, heightened drought frequency, vegetation degradation, and land cover transformation. The findings underline the potential of AI for actionable climate impact assessment and sustainable regional planning.
Nipunge et al. (Thu,) studied this question.
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