2025
3.
Dost, Katharina; Muraoka, Kohji; Ausseil, Anne-Gaelle; Benavidez, Rubianca; Blue, Brendan; Coland, Nic; Daughney, Chris; Semadeni-Davies, Annette; Hoang, Linh; Hooper, Anna; Kpodonu, Theodore Alfred; Marapara, Tapuwa; McDowell, Richard W.; Nguyen, Trung; Nguyet, Dang Anh; Norton, Ned; Özkundakci, Deniz; Pearson, Lisa; Rolinson, James; Smith, Ra; Stephens, Tom; Tamepo, Reina; Taylor, Ken; van Uitregt, Vincent; Jackson, Bethanna; Sarris, Theo; Elliott, Alexander; Wicker, Jörg
Freshwater Quality Modeling in Aotearoa New Zealand: Current Practice and Future Directions Unpublished Forthcoming
SSRN, Forthcoming.
Links | BibTeX | Altmetric | PlumX | Tags: best practice, Catchment modeling process, machine learning, model trustworthiness, Modelling platform design, reliable machine learning, root-cause analysis, water quality
@unpublished{dost2025freshwater,
title = {Freshwater Quality Modeling in Aotearoa New Zealand: Current Practice and Future Directions},
author = {Katharina Dost and Kohji Muraoka and Anne-Gaelle Ausseil and Rubianca Benavidez and Brendan Blue and Nic Coland and Chris Daughney and Annette Semadeni-Davies and Linh Hoang and Anna Hooper and Theodore Alfred Kpodonu and Tapuwa Marapara and Richard W. McDowell and Trung Nguyen and Dang Anh Nguyet and Ned Norton and Deniz \"{O}zkundakci and Lisa Pearson and James Rolinson and Ra Smith and Tom Stephens and Reina Tamepo and Ken Taylor and Vincent van Uitregt and Bethanna Jackson and Theo Sarris and Alexander Elliott and J\"{o}rg Wicker },
doi = {10.2139/ssrn.5105393},
year = {2025},
date = {2025-01-21},
urldate = {2025-01-21},
journal = {SSRN},
howpublished = {SSRN},
keywords = {best practice, Catchment modeling process, machine learning, model trustworthiness, Modelling platform design, reliable machine learning, root-cause analysis, water quality},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
2024
2.
Graffeuille, Olivier; Koh, Yun Sing; Wicker, Jörg; Lehmann, Moritz
Remote Sensing for Water Quality: A Multi-Task, Metadata-Driven Hypernetwork Approach Proceedings Article
In: Larson, Kate (Ed.): Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24), pp. Pages 7287-7295, 2024, (AI for Good).
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: computational sustainability, machine learning, water quality
@inproceedings{graffeuille2024remote,
title = {Remote Sensing for Water Quality: A Multi-Task, Metadata-Driven Hypernetwork Approach},
author = {Olivier Graffeuille and Yun Sing Koh and J\"{o}rg Wicker and Moritz Lehmann },
editor = {Kate Larson},
doi = {10.24963/ijcai.2024/806},
year = {2024},
date = {2024-08-05},
urldate = {2024-08-05},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24)},
pages = {Pages 7287-7295},
abstract = {Inland water quality monitoring is vital for clean water access and aquatic ecosystem management. Remote sensing machine learning models enable large-scale observations, but are difficult to train due to data scarcity and variability across many lakes. Multi-task learning approaches enable learning of lake differences by learning multiple lake functions simultaneously. However, they suffer from a trade-off between parameter efficiency and the ability to model task differences flexibly, and struggle to model many diverse lakes with few samples per task. We propose Multi-Task Hypernetworks, a novel multi-task learning architecture which circumvents this trade-off using a shared hypernetwork to generate different network weights for each task from small task-specific embeddings. Our approach stands out from existing works by providing the added capacity to leverage task-level metadata, such as lake depth and temperature, explicitly. We show empirically that Multi-Task Hypernetworks outperform existing multi-task learning architectures for water quality remote sensing and other tabular data problems, and leverages metadata more effectively than existing methods. },
note = {AI for Good},
keywords = {computational sustainability, machine learning, water quality},
pubstate = {published},
tppubtype = {inproceedings}
}
Inland water quality monitoring is vital for clean water access and aquatic ecosystem management. Remote sensing machine learning models enable large-scale observations, but are difficult to train due to data scarcity and variability across many lakes. Multi-task learning approaches enable learning of lake differences by learning multiple lake functions simultaneously. However, they suffer from a trade-off between parameter efficiency and the ability to model task differences flexibly, and struggle to model many diverse lakes with few samples per task. We propose Multi-Task Hypernetworks, a novel multi-task learning architecture which circumvents this trade-off using a shared hypernetwork to generate different network weights for each task from small task-specific embeddings. Our approach stands out from existing works by providing the added capacity to leverage task-level metadata, such as lake depth and temperature, explicitly. We show empirically that Multi-Task Hypernetworks outperform existing multi-task learning architectures for water quality remote sensing and other tabular data problems, and leverages metadata more effectively than existing methods.
1.
Graffeuille, Olivier; Lehmann, Moritz; Allan, Matthew; Wicker, Jörg; Koh, Yun Sing
Lake by Lake, Globally: Enhancing Water Quality Remote Sensing with Multi-Task Learning Models Unpublished Forthcoming
Forthcoming, ISSN: 1556-5068.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: inland and coastal waters, machine learning, multi-task learning, remote sensing, water quality
@unpublished{graffeuille2024lake,
title = {Lake by Lake, Globally: Enhancing Water Quality Remote Sensing with Multi-Task Learning Models},
author = {Olivier Graffeuille and Moritz Lehmann and Matthew Allan and J\"{o}rg Wicker and Yun Sing Koh },
doi = {10.2139/ssrn.4762429},
issn = {1556-5068},
year = {2024},
date = {2024-03-17},
urldate = {2024-03-17},
abstract = {The estimation of water quality from satellite remote sensing data in inland and coastal waters is an important yet challenging problem. Recent collaborative efforts have produced large global datasets with sufficient data to train machine learning models with high accuracy. In this work, we investigate global water quality remote sensing models at the granularity of individual water bodies. We introduce Multi-Task Learning (MTL), a machine learning technique that learns a distinct model for each water body in the dataset from few data points by sharing knowledge between models. This approach allows MTL to learn water body differences, leading to more accurate predictions. We train and validate our model on the GLORIA dataset of in situ measured remote sensing reflectance and three water quality indicators: chlorophyll$a$, total suspended solids and coloured dissolved organic matter. MTL outperforms other machine learning models by 8-31% in Root Mean Squared Error (RMSE) and 12-34% in Mean Absolute Percentage Error (MAPE). Training on a smaller dataset of chlorophyll$a$ measurements from New Zealand lakes with simultaneous Sentinel-3 OLCI remote sensing reflectance further demonstrates the effectiveness of our model when applied regionally. Additionally, we investigate the performance of machine learning models at estimating the variation in water quality indicators within individual water bodies. Our results reveal that overall performance metrics overestimate the quality of model fit of models trained on a large number of water bodies due to the large between-water body variability of water quality indicators. In our experiments, when estimating TSS or CDOM, all models excluding multi-task learning fail to learn within-water body variability, and fail to outperform a naive baseline approach, suggesting that these models may be of limited usefulness to practitioners monitoring water quality. Overall, our research highlights the importance of considering water body differences in water quality remote sensing research for both model design and evaluation. },
keywords = {inland and coastal waters, machine learning, multi-task learning, remote sensing, water quality},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
The estimation of water quality from satellite remote sensing data in inland and coastal waters is an important yet challenging problem. Recent collaborative efforts have produced large global datasets with sufficient data to train machine learning models with high accuracy. In this work, we investigate global water quality remote sensing models at the granularity of individual water bodies. We introduce Multi-Task Learning (MTL), a machine learning technique that learns a distinct model for each water body in the dataset from few data points by sharing knowledge between models. This approach allows MTL to learn water body differences, leading to more accurate predictions. We train and validate our model on the GLORIA dataset of in situ measured remote sensing reflectance and three water quality indicators: chlorophyll$a$, total suspended solids and coloured dissolved organic matter. MTL outperforms other machine learning models by 8-31% in Root Mean Squared Error (RMSE) and 12-34% in Mean Absolute Percentage Error (MAPE). Training on a smaller dataset of chlorophyll$a$ measurements from New Zealand lakes with simultaneous Sentinel-3 OLCI remote sensing reflectance further demonstrates the effectiveness of our model when applied regionally. Additionally, we investigate the performance of machine learning models at estimating the variation in water quality indicators within individual water bodies. Our results reveal that overall performance metrics overestimate the quality of model fit of models trained on a large number of water bodies due to the large between-water body variability of water quality indicators. In our experiments, when estimating TSS or CDOM, all models excluding multi-task learning fail to learn within-water body variability, and fail to outperform a naive baseline approach, suggesting that these models may be of limited usefulness to practitioners monitoring water quality. Overall, our research highlights the importance of considering water body differences in water quality remote sensing research for both model design and evaluation.