AI in Climate Science: Leveraging XGBoost and Random Forest for Ocean Temperature Forecasting

AI in Climate Science: Leveraging XGBoost and Random Forest for Ocean Temperature Forecasting

 

 

Ms. Ruta Prabhu

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Dr. Anupama Jawale

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Shivwani Nadar

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Meenakshi Konar

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Disha Gandhi

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Heer Panchal

Department of Information Technology,

Narsee Monjee College of Commerce and Economics,

Mumbai,

Maharashtra,

India

 

Dr. Ganesh Magar

SNDT Women's University,

Mumbai,

Maharashtra,

India

 

Abstract:
By examining meteorological and oceanographic variables such as wind patterns, humidity, and air temperature, this study investigates the application of machine learning approaches, Random Forest Regressor and XGBoost Regressor, to forecast sea surface temperature (SST). Using an El Niño dataset, the study applies rigorous preprocessing to integrate spatial-temporal variability and address data discrepancies. Comparative research indicates that XGBoost outperforms Random Forest in terms of prediction accuracy, as seen by higher R2 scores and lower RMSE. The findings provide valuable insights into the ways that oceanic systems are being impacted by climate change and show how advanced machine learning algorithms can capture nonlinear interactions.

Keywords: El Niño, Oceanography, Random Forest, XGBoost, etc