A Hybrid Framework for Predicting Global Temperature Anomalies Using Machine Learning and Deep Learning

Overview

This repository contains a Jupyter Notebook implementing a hybrid framework for predicting global temperature anomalies. The framework leverages machine learning and deep learning models to analyze historical climate data from various sources.

Data Sources

The following datasets are used for analysis:

• Extended Reconstructed Sea Surface Temperature (ERSST)
• Global Historical Climatology Network monthly (GHCNm)
• International Comprehensive Ocean-Atmosphere Data Set (ICOADS)
• International Arctic Buoy Programme (IABP)

Spatial Coverage

The dataset provides global 5° × 5° spatial resolution.

Dependencies

To run the notebook, install the required Python libraries:

pip install xarray netCDF4 tensorly

Usage

1. Clone this repository:

   git clone https://github.com/AyobamiMichael/HybridframeworkInAction.git
   cd HybridframeworkInAction

2. Open the Jupyter Notebook:

   jupyter notebook hybridframework1.ipynb

3. Execute the cells in order to preprocess the data, train models, and analyze predictions.

Features

• Uses xarray for handling climate datasets.
• Implements machine learning and deep learning models for prediction.
• Supports tensor decomposition techniques for feature extraction.

License

This project is open-source and available under the MIT License.

Author

[Ayobami Opefeyijimi] - [[email protected]]