https://github.com/javedali99/machine-learning-final-project

Storm Surge Prediction Using Different Machine Learning Methods
https://github.com/javedali99/machine-learning-final-project

machine-learning machine-learning-algorithms machinelearning-python ml prediction prediction-model predictive-modeling python sea-level-rise storm-surge storm-surge-modelling

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Storm Surge Prediction Using Different Machine Learning Methods

• Host: GitHub
• URL: https://github.com/javedali99/machine-learning-final-project
• Owner: javedali99
• License: mit
• Created: 2021-03-24T19:30:13.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2023-11-05T18:08:04.000Z (over 2 years ago)
• Last Synced: 2023-11-05T19:23:37.036Z (over 2 years ago)
• Topics: machine-learning, machine-learning-algorithms, machinelearning-python, ml, prediction, prediction-model, predictive-modeling, python, sea-level-rise, storm-surge, storm-surge-modelling
• Language: Jupyter Notebook
• Homepage:
• Size: 12.3 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Storm Surge Prediction Using Different Machine Learning Methods

**Description**

- [Storm surge prediction using ML.ipynb](https://github.com/javedali99/machine-learning-final-project/blob/main/Storm%20surge%20prediction%20using%20ML.ipynb) - This file includes:

  - Data preprocessing 

    - Data cleaning, feature selection and creating time lagging predictors data

    - Data preparation (predictor and predictand data)

    - Splitting the data into training and testing datasets

    - Standardizing the training and testing datasets

  - Autocorrelation in time series

  - Multi Layer Perceptron (MLP)

    - Building a MLP sequential model, Training the model, Model evaluation and Plotting the results

    - MLP Hyperparameter Tuning

  - Long Short-Term Memory Networks (LSTM)

    - Building LSTM model, Data preparation, Training the model, Model evaluation and Plotting the results

  - Auto Regressive Integrated Moving Average (ARIMA)

    - Model building and analysis

    - Forecasting

    - Model evaluation

    - Results visualization 

  - Convolutional Neural Networks (CNN)

- [projectML.py](https://github.com/javedali99/machine-learning-final-project/blob/main/projectML.py) - This file includes

  - Data preprocessing

  - Random Forest Regression (RFR)

    - Data preprocessing

    - Training the model

    - Model evaluation

    - Results visualization

  - Support Vector Regression (SVR)

    - Data preprocessing

    - Training the model

    - Linear, RBF and Polynomial kernels for SVM

    - Model evaluation

    - SVR hyper parameters tuning

    - Results visualization

    - Improvement of the SVR method by changing temporal resolution to "daily max surge" instead of hourly