Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envsoft.2026.107050 |
Licence  |
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| Title (Primary) | Short-term forecasting of daily dissolved oxygen in streams using SWAT, remote sensing and explainable machine learning |
| Author | Dang, T.D.; Hoang, L.; Woodward, K.B.; Nguyen, V.T.
; Elliott, A.H. |
| Source Titel | Environmental Modelling & Software |
| Year | 2026 |
| Department | HDG |
| Volume | 204 |
| Page From | art. 107050 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | Dissolved oxygen; SWAT; machine learning; remote sensing; forecasting |
| Abstract | Dissolved
oxygen (DO) concentration is a key indicator of aquatic ecosystem
health yet modelling and forecasting it accurately at daily resolution
remains challenging due to the complex interplay of influencing factors.
In this study, we explore the potential of combining diverse data
sources, including in-situ observations, satellite-derived time series,
and modelled catchment flow and nutrient loads from SWAT models, to
forecast daily DO concentrations in streams. This is the first study
that integrates SWAT-simulated hydrological and nutrient outputs with
remote sensing data and machine learning to forecast daily DO in
streams, advancing beyond previous models that rely solely on
observations or use models only for DO simulation. We evaluated the
performance of three widely used machine learning and deep learning
models (Random Forest, LSTM, and Transformer) at three stream sites in
the Upper Piako catchment in New Zealand. The results showed that model
performance varied by site, emphasizing the importance of input
variables tailored to local conditions. While water temperature
consistently emerged as a dominant predictor, other variables such as
baseflow and vegetation indices also were found to be important
predictors. Our findings highlight the importance of integrating domain
knowledge to guide feature selection, particularly when combining
observational, remote sensing, and modelled data, to improve DO
forecasting accuracy. |
| Dang, T.D., Hoang, L., Woodward, K.B., Nguyen, V.T., Elliott, A.H. (2026): Short-term forecasting of daily dissolved oxygen in streams using SWAT, remote sensing and explainable machine learning Environ. Modell. Softw. 204 , art. 107050 10.1016/j.envsoft.2026.107050 |
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