Assessing the applicability of Transformer-based architectures as rainfall-runoff models

Modeling the relationship between rainfall and runoff is a longstanding challenge in hydrology and is crucial for informed water management decisions. Recently, Deep Learning models, particularly Long short-term memory (LSTM), have shown promising results in simulating this relationship. The Transformer, a newly proposed deep learning...

Assessing the impact of climate change on streamflow in catchments across the United States, using an easily reproducible modeling approach

In this thesis, an easily reproducible modeling approach was developed for assessing the climate change impact on streamflow. This approach was tested by using it to assess the impact of climate change on streamflow in 5 different contrasting catchments across the United States. Many studies show that climate change is expected to influence...

Assessing Global Applicability of a Long Short-Term Memory (LSTM) Neural Network for Rainfall-Runoff Modelling

Rainfall-runoff modelling is essential for short- and long-term decision-making in the water management sector. The accuracy of streamflow predictions of hydrologic models increases with the availability of and the access to streamflow observations. Therefore, one of the key challenges in the field of hydrology is to produce Predictions in...