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Browse filesDrought Prediction Using Satellite Imagery
This repository contains code and resources for predicting drought using satellite imagery. The goal of this project is to develop a machine learning model that leverages satellite data to forecast and assess drought conditions in a given region.
Dataset
The dataset used for training and evaluation is a collection of satellite imagery data, combined with historical drought data. The dataset includes various spectral bands, indices, and meteorological variables captured by satellites over time. The data preprocessing steps are described in the repository's documentation.
Model Architecture
The repository implements a state-of-the-art deep learning architecture for drought prediction. The model takes satellite imagery data as input and employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to learn spatio-temporal patterns and make accurate predictions.
Usage
To use the code in this repository, follow the instructions provided in the documentation. It includes steps for data preprocessing, model training, and prediction. Sample scripts and Jupyter notebooks are provided to assist users in understanding and applying the models to their own datasets.
Results
The repository showcases the performance of the trained model on drought prediction tasks.
Contributions and Feedback
Contributions, bug reports, and feature requests are welcome! If you encounter any issues or have ideas for improvement, please open an issue or submit a pull request. Your feedback will help enhance the effectiveness and usability of the drought prediction model.
License
The code and resources in this repository are released under the Jimma University License. Feel free to use, modify, and distribute the code for your own projects or research purposes.