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Advancing Cloudburst Prediction with Mixed-Input Deep Learning: Combining CNN and Feed-Forward Neural Networks

Train a multi-modal deep learning scheme (combining CNN and FNN) to predict cloudburst events using INSAT3D images and ERA5 hourly temperature and precipitation data.

Input data

  • Satellite images from INSAT3D collected from Meteorological and Oceanographic Satellite Data Archival Centre(https://mosdac.gov.in/) during the occurrence of cloud bursts, as well as 6 hours prior and 6 hours after the cloudburst. Images of random noncloudburst events
  • satellite images were labeled into 4 categories: 'cloudburst,' 'precloudburst', 'postcloudburst', and 'no cloudburst
  • ERA5 Climate reanalysis data (hourly temperature and precipitation) from CDS store (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form) converted into .csv format

Functionality

  • Preprocess data: the script preprocess corresponding spatial satellite images and tabular climate data from ERA5 and merge them
  • Splitting into train, validation, and test set : the processed data is shuffled and split into train, validation, and test dataset
  • Model architecture : a model architecture is defined by combining a CNN (MobileNetV2) and FNN
  • Model optimization: the defined model is optimized using ADAM algorithm in 10 epochs and model loss and accuracy were collected for each epoch
  • Visualization : model loss and accuracy are plotted to visualize if model accuracy is getting better with each epoch
  • Model testing : the best performing trained model is tested on the test dataset and model accuracy and loss on test dataset are printed

Future scope

  • The trained multi-model DL scheme can be fed with real-time satellite images and weather data to predict cloudburst risk.
  • If a cloudburst risk is detected, automatic notifications can be sent to concerned authorities for disaster readiness and evacuation planning.

Dependencies

  • Pandas
  • Numpy
  • cv2
  • Matplotlib
  • TensorFlow

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