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Storm Surge Prediction Using Different Machine Learning Methods

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javedali99/machine-learning-final-project

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Storm Surge Prediction Using Different Machine Learning Methods

Description

  • Storm surge prediction using ML.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 - 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