| Challenges in applying machine learning models for hydrological inference: A case study for flooding events across Germany L Schmidt, F Heße, S Attinger, R Kumar Water resources research 56 (5), e2019WR025924, 2020 | 95 | 2020 |
| System for automated Quality Control (SaQC) to enable traceable and reproducible data streams in environmental science L Schmidt, D Schäfer, J Geller, P Lünenschloss, B Palm, K Rinke, ... Environmental Modelling & Software 169, 105809, 2023 | 3 | 2023 |
| From source to sink-Sustainable and reproducible data pipelines with SaQC D Schäfer, B Palm, L Schmidt, P Lünenschloß, J Bumberger EGU General Assembly Conference Abstracts, 19648, 2020 | 2 | 2020 |
| Spatially-distributed Deep Learning for rainfall-runoff modelling and system understanding L Schmidt, E Gusho, W de Back, K Vinogradova, R Kumar, O Rakovec, ... EGU General Assembly Conference Abstracts, 20736, 2020 | 2 | 2020 |
| Interpretable Quality Control of Sparsely Distributed Environmental Sensor Networks Using Graph Neural Networks EK Lasota, T Houben, J Polz, L Schmidt, L Glawion, D Schäfer, ... EarthArXiv, 2024 | | 2024 |
| Leveraging the Power of Graph Neural Networks in Environmental Time Series Anomaly Detection E Lasota, J Polz, T Houben, L Schmidt, D Schäfer, J Bumberger, ... EGU24, 2024 | | 2024 |
| SaQC: Empowering Hydrological Data Integrity through Automated Quality Control D Schäfer, P Lünenschloß, B Palm, L Schmidt, T Schnicke, C Rebmann, ... EGU24, 2024 | | 2024 |
| Tackling practical challenges in anomaly detection for real-time monitoring of urban waste water networks L Schmidt, F Weiske, M Schütze, P Grimm, J Polz, J Bumberger EGU24, 2024 | | 2024 |
| Expert Flagging of Commercial Microwave Link Signal Anomalies: Effect on Rainfall Estimation and Ambiguity of Flagging J Polz, L Glawion, M Graf, N Blettner, E Lasota, L Schmidt, H Kunstmann, ... 2023 IEEE International Conference on Acoustics, Speech, and Signal …, 2023 | | 2023 |
| Enhancing environmental sensor data quality control with graph neural networks E Lasota, J Polz, C Chwala, L Schmidt, P Lünenschloß, D Schäfer, ... EGU General Assembly Conference Abstracts, EGU-9434, 2023 | | 2023 |
| Reproducible quality control of time series data with SaQC D Schäfer, B Palm, P Lünenschloß, L Schmidt, J Bumberger EGU General Assembly Conference Abstracts, EGU-12971, 2023 | | 2023 |
| Machine learning-based anomaly detection for real-time monitoring of urban waste water networks L Schmidt, F Weise, M Schütze, P Grimm, J Polz, J Bumberger EGU23, 2023 | | 2023 |
| Machine learning based spatio-temporal interpolation of soil moisture in an agricultural catchment S Khurana, T Houben, P Ebeling, J Schmid, L Schmidt, M Anand, J Boog AGU Fall Meeting Abstracts 2021, H55C-0775, 2021 | | 2021 |
| A new distributed data analysis framework for better scientific collaborations PS Sommer, V Wichert, D Eggert, T Dinter, K Getzlaff, A Lehmann, ... EGU General Assembly Conference Abstracts, EGU21-1614, 2021 | | 2021 |
| Supervised and unsupervised machine-learning for automated quality control of environmental sensor data J Polz, L Schmidt, L Glawion, M Graf, C Werner, C Chwala, ... EGU21, 2021 | | 2021 |
| On the potential and challenges of using machine-learning for automated quality control of environmental sensor data L Schmidt, H Mollenhauer, C Rebmann, D Schäfer, A Claussnitzer, ... EGU General Assembly Conference Abstracts, 20777, 2020 | | 2020 |
| Controls of flood magnitude: A Germany-wide analysis using parametric and non-parametric approaches L Schmidt Chair of Hydrology, University of Freiburg, 2018 | | 2018 |
