2025
Cheena, Asif; Dost, Katharina; Sarris, Theo; Straathof, Nina; Wicker, Jörg
Don't Swim in Data: Real-Time Microbial Forecasting for New Zealand Recreational Waters Unpublished Forthcoming
SRRN, Forthcoming.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: computational sustainability, machine learning, matrix decomposition, time series forecasting, water quality
@unpublished{Cheena2025dont,
title = {Don\'t Swim in Data: Real-Time Microbial Forecasting for New Zealand Recreational Waters},
author = {Asif Cheena and Katharina Dost and Theo Sarris and Nina Straathof and J\"{o}rg Wicker},
doi = {10.2139/ssrn.5230457},
year = {2025},
date = {2025-04-30},
urldate = {2025-04-30},
abstract = {Traditional water quality monitoring, reliant on infrequent sampling and 48-hour laboratory delays, often fails to capture rapid contamination fluctuations, exposing recreational water users to significant health risks. We propose two novel machine learning frameworks for real-time forecasting of Enterococci concentrations in Canterbury, New Zealand. The Probabilistic Forecasting Framework uses an ensemble of quantile regression models with a gradient boosting meta-learner and Conformalized Quantile Regression (CQR) to produce accurate point forecasts and calibrated 90% prediction intervals. In parallel, the Matrix Decomposition Framework employs Non-negative Matrix Factorization (NMF) to decompose spatio-temporal data into interpretable latent factors, modeled via multi-target Random Forests to enhance generalizability. Evaluated on data from 15 sites (2021\textendash2024, 1047 samples, 100 exceedance events), our frameworks exceed USGS guidelines, achieving exceedance sensitivities of 67.0% and 61.0%, with high precautionary sensitivities of 77.0% and 74.0%, respectively, and competitive performance relative to state-of-the-art systems such as Auckland’s Safeswim.},
howpublished = {SRRN},
keywords = {computational sustainability, machine learning, matrix decomposition, time series forecasting, water quality},
pubstate = {forthcoming},
tppubtype = {unpublished}
}
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}
}