<![CDATA[This study undertakes a comparative analysis of machine learning and deep learning models for forecasting the impacts of climate change, utilizing Cross-Validation Root Mean Squared Error (CV RMSE) to gauge performance. Analyzed models include Long Short-Term Memory (LSTM) networks (CV RMSE: 0.155), Linear Regression (CV RMSE: 5647815244.91), Random Forest (CV RMSE: 0.159), Gradient Boosting Machine (GBM) (CV RMSE: 0.164), Support Vector Regressor (SVR) (CV RMSE: 0.159), Decision Tree Regressor (CV RMSE: 0.199), and K-Nearest Neighbors (KNN) Regressor (CV RMSE: 0.166). The study rigorously processes climate change time series data to ensure accurate, generalizable results. LSTM networks demonstrated exceptional performance, indicating their strong capacity for modeling complex temporal sequences inherent in climate data, while Linear Regression lagged significantly behind, revealing limitations in addressing non-linear patterns of climate change. The promising results of Random Forest and SVR models suggest their applicability in environmental science forecasting tasks. Our findings offer valuable insights into the efficacy of various predictive models, aiding researchers and policymakers in leveraging advanced analytics for climate change mitigation strategies]]>
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