Analyzing the performance of transformers for streamflow prediction

Within the field of hydrology, there is a vital need to be able to predict streamflow values from hydrological basins. This has traditionally been done through physics and mathematics-based models, where measured data are combined with physics-based formulas to estimate output values. Nowadays, machine learning has been introduced as a potential way to improve the performance of these predictions. One of the classical methods for time-series prediction has been the Long Short Term Memory (LSTM) model, but the transformer model has also shown its ability to be proficient at solving these kinds of problems. The purpose of this paper is to implement a transformer model within an existing model…