doi: 10.18178/ijesd.2025.16.5.1543
Predicting River Nitrate (NO₃) Concentrations Using Hybrid Deep Learning: Case Study of the Willamette River, Oregon
Ismail Farajpour1,*
,
Sina Davoudi2
- 1Department of the Built Environment, University of Maryland Eastern Shore, Thomas Briggs Arts and Technology Center, Princess Anne, MD 21853, USA
- 2Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Iran
- Manuscript receivedFebruary 25, 2025
- revisedApril 7, 2025
- acceptedJune 16, 2025
- publishedSeptember 19, 2025
Abstract
Predicting nitrate levels in rivers is vital formaintaining healthy ecosystems, as excessive nitrates candisrupt aquatic life and lead to harmful algal blooms. Accuratenitrate monitoring also ensures safe drinking water, protectingcommunities from health risks. To keep our rivers healthy,protect water resources, and ensure human safety, accuratelymeasuring nitrate levels—a key sign of water quality—isessential. We developed three advanced models to predicthourly nitrate levels in the Willamette River in Portland,Oregon, using data from 2021–2022. These models were a GatedRecurrent Unit (GRU), a Convolutional Neural Network (CNN),and a new hybrid model combining both, called CNN-GRU. Wetested their performance using metrics like correlationcoefficient (R), Determination Coefficient (DC), and Root MeanSquared Error (RMSE). The models used six key water qualityfactors: river flow, turbidity (cloudiness), water velocity, specificconductance, dissolved oxygen, and water temperature. Thehybrid CNN-GRU model performed best, with an R-value of0.876, a DC of 0.761, and an RMSE of 0.086, outshining thestandalone models. Among the individual models, GRU didbetter than CNN. We also found that turbidity was the mostcritical factor for predicting nitrate levels.
Keywords
- water quality
- nitrate prediction
- deep learning
- CNN
- GRU
How to Cite
Ismail Farajpour and Sina Davoudi, "Predicting River Nitrate (NO₃) Concentrations Using Hybrid Deep Learning: Case Study of the Willamette River, Oregon," International Journal of Environmental Science and Development, vol. 16, no. 5, pp. 345-352, 2025. https://doi.org/10.18178/ijesd.2025.16.5.1543
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).