https://github.com/sarwanshah/review-on-rnns-lstms-for-precipitation-nowcasting-2024

In this project we worked on developing an understanding of RNN and LSTM machine learning paradigms and their application in precipitation nowcasting
https://github.com/sarwanshah/review-on-rnns-lstms-for-precipitation-nowcasting-2024

deep-learning literature-review lstm machine-learning mathematical-modelling precipitation-nowcasting rnn

Last synced: 4 months ago
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In this project we worked on developing an understanding of RNN and LSTM machine learning paradigms and their application in precipitation nowcasting

• Host: GitHub
• URL: https://github.com/sarwanshah/review-on-rnns-lstms-for-precipitation-nowcasting-2024
• Owner: SarwanShah
• License: cc0-1.0
• Created: 2025-02-09T03:57:01.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-02-10T00:38:20.000Z (8 months ago)
• Last Synced: 2025-06-16T02:06:12.863Z (4 months ago)
• Topics: deep-learning, literature-review, lstm, machine-learning, mathematical-modelling, precipitation-nowcasting, rnn
• Homepage:
• Size: 7.64 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Understanding RNNs and LSTMs for Precipitation Nowcasting

## Project Overview  

This project was completed as a part of **EEE-560 Mathematical Foundations of Machine Learning** at **Arizona State University** during Spring 2024. This project explores the theoretical foundation, architecture, and applications of **Recurrent Neural Networks (RNNs)** and **Long Short-Term Memory (LSTM)** networks, with a specific focus on their use in **precipitation nowcasting**. The study emphasizes the mathematical modeling, training methodologies, and challenges of these neural network architectures.

**REPORT: [Final Paper](./SSHAH219-EEE560-Final-Paper.pdf)**

## Project Features  

- **Neural Network Fundamentals**: Review of basic neural networks, including feed-forward architectures.

- **RNN and LSTM Modeling**: Analysis of temporal relationships in sequential data through RNNs and their architectural variant, LSTMs.

- **Challenges in Training**: Exploration of issues like vanishing and exploding gradients and methods to mitigate them.

- **Applications in Meteorology**: Use of LSTMs and ConvLSTMs for short-term precipitation predictions.

- **Advanced Architectures**: Overview of extensions such as **ConvLSTM** and **TrajGRU** for improved spatial and temporal modeling.

## Implementation Details  

### ➤ **Neural Network Architecture**  

- **Single and Multi-Layer Networks**: Explains how multi-layer architectures capture complex patterns in data through non-linear activation functions.

- **Training via Backpropagation**: Describes how neural networks are trained using **gradient descent** and backpropagation.

### ➤ **RNNs**  

- **Temporal Layering**: Captures sequential dependencies by maintaining shared weights across time steps.

- **Backpropagation Through Time (BPTT)**: Modifies standard backpropagation to handle temporal sequences.

### ➤ **LSTMs**  

- **Cell States**: Introduces stable long-term memory storage to address gradient issues.

- **Gate Mechanisms**: Explains how input, forget, and output gates regulate hidden states and memory.

### ➤ **Applications in Precipitation Nowcasting**  

- **Short-Term Weather Forecasting**: Highlights the use of LSTMs for predicting precipitation within a 0 to 6-hour window.

- **ConvLSTM**: Enhances LSTM by adding spatial awareness through convolutional operations.

- **TrajGRU**: Introduces location-variant connections to improve modeling of motion patterns like rotation and scaling.

## Key Insights  

- Neural networks require **non-linear activation functions** to model complex patterns.

- RNNs and LSTMs excel at handling short-term sequential data but face challenges with long-term sequences.

- Advanced architectures like **ConvLSTM** and **TrajGRU** enhance both spatial and temporal modeling capabilities, making them well-suited for precipitation nowcasting.

This project contributes to the understanding of advanced neural network architectures and their application in real-world scenarios, particularly in meteorology.