Published in eartharxiv, 2024
Recommended citation: Janssen, J., Tootchi, A., & Ameli, A. A. (2024). A critical appraisal of water table depth estimation: Challenges and opportunities within machine learning. https://eartharxiv.org/repository/view/7067/
Published in Arxiv, 2023
Recommended citation: Schrunner, S., Janssen, J., Jenul, A., Cao, J., Ameli, A. A., & Welch, W. J. (2023). A Gaussian Sliding Windows Regression Model for Hydrological Inference. arXiv preprint arXiv:2306.00453. https://arxiv.org/abs/2306.00453
Published in arxiv, 2023
Recommended citation: Bilolikar, D. K., More, A., Gong, A., & Janssen, J. (2023). How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology. arXiv preprint arXiv:2305.07136. https://arxiv.org/abs/2305.07136
Published in AISTATS, 2023
Recommended citation: Janssen, J., Guan, V., & Robeva, E. (2023, April). Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees. In International Conference on Artificial Intelligence and Statistics (pp. 10782-10814). PMLR. https://proceedings.mlr.press/v206/janssen23a.html
Published in Arxiv, 2023
Recommended citation: Janssen, J., Meng, S., Haris, A., Schrunner, S., Cao, J., Welch, W. J., ... & Ameli, A. A. (2023). Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science. arXiv preprint arXiv:2303.06501. https://arxiv.org/abs/2303.06501
Published in Hydrology and Earth System Sciences Discussions, 2022
Recommended citation: Le, E., Ameli, A., Janssen, J., & Hammond, J. (2022). Snow persistence explains stream high flow and low flow signatures with differing relationships by aridity and climatic seasonality. Hydrology and Earth System Sciences Discussions, 1-22. https://hess.copernicus.org/preprints/hess-2022-106/
Published in Frontiers in Earth Science, 2021
Recommended citation: Janssen, J., Radić, V., & Ameli, A. (2021). Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America. Frontiers in Earth Science, 9, 730631. https://www.frontiersin.org/articles/10.3389/feart.2021.730631/
Published in Water Resources Research, 2021
Recommended citation: Janssen, J., & Ameli, A. A. (2021). A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions. Water Resources Research, 57(9), e2021WR030263. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR030263