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

Contributor

• Pramit De (email: [email protected]/[email protected]) (Department of CSE, Techno International New Town, West Bengal, India)