Forecasting Climate Extremities: Leveraging Gradient Boosting Machines for Accurate Temperature Predictions

Author Reg. ID: TIJER_151392

Published Paper Id: TIJER2403063

Published In: Volume 11 Issue 3, March-2024

Abstract: Climate extremities pose significant challenges globally, necessitating accurate prediction methodologies. This research employs Gradient Boosting Machine (GBM) algorithms to predict climate extremities. Utilizing a dataset encompassing annual surface temperature changes, the study investigates patterns and correlations among variables. Initial exploratory data analysis reveals insights into temperature trends and distributions. Subsequently, a GBM model is implemented for prediction, achieving high accuracy (96.875%). The model's efficacy is evaluated through metrics such as Mean Squared Error and R-squared. Furthermore, the distribution of residuals highlights model performance. This research contributes to climate science by offering a robust predictive tool for climate extremities, aiding in risk mitigation and policy formulation.

Keywords: Climate extremities, Prediction, Gradient Boosting Machine (GBM), Temperature trends, Risk mitigation.

Downloads: 00029

Page No: a471-a481

Country: Vasai/Palghar, Mahrashtra, India

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403063

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403063

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