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.
Harvesting Ship’s Speed and Environmental Information
The MariData project was recently launched to improve the energy efficiency of ship operations and to reduce emissions. Fuel consumption regarding vessel traffic is affected by many factors, namely, the main and auxiliary engines, the propulsion system, ship hull, propellers, seakeeping performance, as well as weather and sea conditions. In order to tackle these problems, we divide the task of building a weather routing system into subproblems and handle each subproblem separately. Finding the correlations between the speed of the vessel and weather conditions would lead to a better understanding of the weather conditions affecting fuel consumption, therefore, we obtained speed data of the ship’s Automatic Identification Systems (AIS), then retrieved weather and sea information for each data point using timestamp and geographic locations. The combination of these data sources enabled us to build a model to better understand the inherent relationships.