Flood Prediction in Kenya - Leveraging Pre-Trained Models to Generate More Validation Data in Sparse Observation Settings (Proposals Track)
Alim Karimi (Self / Purdue University); David Quispe (University of Toronto); Hammed Akande (Concordia University); Nicole Mong'are (Athi Water Works Development Agency); Valerie Brosnan (Mitga Solutions); Asbina Baral (Ministry of Education, Science and Technology)
Abstract
Kenya has lacked a national flood risk management framework and also has sparse flood observation data, which makes developing deep learning flood prediction models on a national scale challenging. Flood prediction models are critical to operationalize Early Warning Systems (EWS). We propose two different models to feed into an EWS. The first model will leverage statistical machine learning approaches to predict flood or no flood events on a 0.25 x 0.25 degree scale (approximately 30 km x 30 km in Kenya) using ERA5 features as well as land cover and Digital Terrain Model (DTM) data. This first model will also be used to create a baseline prediction benchmark across the entire country of Kenya. The second model will leverage pre-trained remote sensing based models to generate segmented flood or no flood data on a fine spatial scale. This will increase the number of validation points by a factor of 1000, which opens the door to deep learning approaches to predict flood or no-flood events on a 30 meter x 30 meter spatial scale. We hope that this approach of leveraging pre-trained models to generate fine scale validation data can eventually be used widely in other extreme climate event forecasting scenarios given the scarcity of historical extreme climate events compared to normal weather events.