To address these limitations, we will explore a novel approach to geospatial data search using a Large Language Model (LLM) based framework in this blog post.
Hydrological time series forecasting with Neural Networks
Bochum students explore deep learning techniques for rainfall runoff predictions
In recent years, the applicability of Machine Learning approaches for hydrological use cases has reached great attendance. Especially long short-term memory (LSTM) networks show comparable results to conceptual models when being applied to hydrological time series forecasting problems. Now, Interdisciplinary students from Bochum explored innovative deep learning techniques for rainfall-runoff modeling.