Fine-tuning a globally pre-trained LSTM on data from individual basins raises mean NSE by 14% and mean KGE by 15% on 159 Caravan basins, with the largest gains in the basins the global model handles worst.
A cost effective solution to reduce disaster losses in developing countries: Hydro-meteorological services, early warning, and evacuation
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Fine Flood Forecasts: Incorporating local data into global models through fine-tuning
Fine-tuning a globally pre-trained LSTM on data from individual basins raises mean NSE by 14% and mean KGE by 15% on 159 Caravan basins, with the largest gains in the basins the global model handles worst.