River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model
The supersaturation of total dissolved gas (TDG) in rivers serves as a critical indicator of water quality downstream of high dams. This study models TDG levels at two monitoring stations in the Columbia and Snake River Basins (USA), where high TDG concentrations were recorded. Hourly data on water...
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002006 |
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| author | Khabat Khosravi Salim Heddam Changhyun Jun Sayed M. Bateni Dongkyun Kim Essam Heggy |
| author_facet | Khabat Khosravi Salim Heddam Changhyun Jun Sayed M. Bateni Dongkyun Kim Essam Heggy |
| author_sort | Khabat Khosravi |
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| description | The supersaturation of total dissolved gas (TDG) in rivers serves as a critical indicator of water quality downstream of high dams. This study models TDG levels at two monitoring stations in the Columbia and Snake River Basins (USA), where high TDG concentrations were recorded. Hourly data on water temperature, barometric pressure, dam spill, sensor depth, and discharge serve as input variables for deep-learning models. Several models are developed and tested, including long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and an alternating model tree (AMT) hybridized with iterative absolute error regression (IAER) and iterative classifier optimizer (ICO) algorithms. A greedy stepwise feature selection technique (GSFST) is employed to identify the optimal model inputs. Each model is trained and evaluated at one station and validated at the second station to assess transferability and generalization capability. Model performance was compared using multiple quantitative and qualitative metrics, including the Nash–Sutcliffe Efficiency and uncertainty coefficient. Additionally, Friedman and Wilcoxon signed-rank tests confirmed statistically significant differences between models. Dam spills emerged as the most influential predictor of TDG levels at both sites. The GSFST selected the optimal input combination, including dam spill, water temperature, barometric pressure, and sensor depth. Among all models, GSFST-BiLSTM achieved the highest predictive accuracy, with Nash–Sutcliffe values of 0.95 (testing) and 0.90 (validation) and uncertainty coefficients of 5.2 % and 7.0 %, respectively. These findings demonstrate that GSFST-BiLSTM provides a robust and transferable framework for TDG prediction, with the potential for broader application pending further validation. |
| format | Article |
| id | doaj-art-d628f832ab6544e7bd7a3711d5aca254 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-d628f832ab6544e7bd7a3711d5aca2542025-08-20T05:05:00ZengElsevierEcological Informatics1574-95412025-12-019010319110.1016/j.ecoinf.2025.103191River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory modelKhabat Khosravi0Salim Heddam1Changhyun Jun2Sayed M. Bateni3Dongkyun Kim4Essam Heggy5Department of Natural Resources, College of Agriculture and Natural Resources, Razi University, Kermanshah, Iran; Corresponding authors.Faculty of Science, Agronomy Department, University 20 Août 1955 Skikda, Route El Hadaik, BP 26 Skikda, AlgeriaSchool of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University, Seoul, Republic of Korea; Corresponding authors.Department of Civil, Environmental and Construction Engineering, and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA; UNESCO-UNISA Africa Chair in Nanoscience and Nanotechnology College of Graduates Studies, University of South Africa, Muckleneuk Ridge, Pretoria 392, South AfricaDepartment of Civil and Environmental Engineering, Hongik University, Seoul, South KoreaViterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USAThe supersaturation of total dissolved gas (TDG) in rivers serves as a critical indicator of water quality downstream of high dams. This study models TDG levels at two monitoring stations in the Columbia and Snake River Basins (USA), where high TDG concentrations were recorded. Hourly data on water temperature, barometric pressure, dam spill, sensor depth, and discharge serve as input variables for deep-learning models. Several models are developed and tested, including long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and an alternating model tree (AMT) hybridized with iterative absolute error regression (IAER) and iterative classifier optimizer (ICO) algorithms. A greedy stepwise feature selection technique (GSFST) is employed to identify the optimal model inputs. Each model is trained and evaluated at one station and validated at the second station to assess transferability and generalization capability. Model performance was compared using multiple quantitative and qualitative metrics, including the Nash–Sutcliffe Efficiency and uncertainty coefficient. Additionally, Friedman and Wilcoxon signed-rank tests confirmed statistically significant differences between models. Dam spills emerged as the most influential predictor of TDG levels at both sites. The GSFST selected the optimal input combination, including dam spill, water temperature, barometric pressure, and sensor depth. Among all models, GSFST-BiLSTM achieved the highest predictive accuracy, with Nash–Sutcliffe values of 0.95 (testing) and 0.90 (validation) and uncertainty coefficients of 5.2 % and 7.0 %, respectively. These findings demonstrate that GSFST-BiLSTM provides a robust and transferable framework for TDG prediction, with the potential for broader application pending further validation.http://www.sciencedirect.com/science/article/pii/S1574954125002006Total dissolved gas (TDG)PredictionDeep learningBidirectional long short-term memory (BiLSTM)Greedy stepwise feature selectionFriedman test |
| spellingShingle | Khabat Khosravi Salim Heddam Changhyun Jun Sayed M. Bateni Dongkyun Kim Essam Heggy River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model Ecological Informatics Total dissolved gas (TDG) Prediction Deep learning Bidirectional long short-term memory (BiLSTM) Greedy stepwise feature selection Friedman test |
| title | River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model |
| title_full | River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model |
| title_fullStr | River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model |
| title_full_unstemmed | River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model |
| title_short | River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model |
| title_sort | river total dissolved gas prediction using a hybrid greedy stepwise feature selection and bidirectional long short term memory model |
| topic | Total dissolved gas (TDG) Prediction Deep learning Bidirectional long short-term memory (BiLSTM) Greedy stepwise feature selection Friedman test |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002006 |
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