Application of LSTMs and HAND in Rapid Flood Inundation Mapping

Every year, floods cause billions of dollars' worth of damages to life, crops, and property. With a proper early flood warning system in place, decision-makers can take the necessary steps to prevent or at least mitigate the damage caused by floods. Although various flood prediction models exist, a majority of them fail to be fast, reliable, and detailed simultaneously. The proposed system presents a novel hybrid flood prediction model using Long Short Term Memory(LSTM) for multivariate time series forecasting of water depth based on meteorological conditions and Height Above Nearest Drainage(HAND) to predict river stage in real-time and map the inundated areas for the corresponding water depth using enhanced HAND. Unlike traditional flood forecasting models, this hybrid approach is resource efficient and easy to implement making it highly practicable for real-time flood inundation mapping.