TOWARDS LEARNING UNIVERSAL, REGIONAL, AND LOCAL HYDROLOGICAL BEHAVIORS VIA MACHINE LEARNING APPLIED TO LARGE-SAMPLE DATASETS

Abstract

In the realm of hydrological sciences, the challenge of accurately modelling regional rainfall–runoff remain a tough problem , especially when dealing with multiple basins. This study introduces a sophisticated statistics-pushed technique by using Long Short-Term Memory networks (LSTM) and it’s adaptation called , Entity-Aware LSTMs (EA-LSTMs), to deal with the complexity of multi-basin hydrological modelling. Using an in depth dataset called CAMELS dataset, which contains daily meteorological data and static catchment attributes from multiple basins, we trained and developed LSTM model along with its EA-LSTM enhancement. This procedure showcased tremendous enhancements in understanding and predicting basin-precise hydrological responses, outperforming the overall performance of traditional hydrological models. The EA-LSTM model, specifically, includes catchment’s static attributes , allowing it to carefully look at and replicate the precise hydrological behaviour of every basin. This integration results in a strong version that no longer outperforms the traditional LSTM in accuracy and performance but also offers deeper insights into the hydrological strategies through mapping catchment attributes. Our findings spotlight the ability of these superior neural community strategies to revolutionize nearby hydrological modelling, imparting a pathway to more specific and green forecasting device for complicated hydrological structures.