River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model

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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Bibliographic Details
Main Authors: Khabat Khosravi, Salim Heddam, Changhyun Jun, Sayed M. Bateni, Dongkyun Kim, Essam Heggy
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Ecological Informatics
Subjects:
Total dissolved gas (TDG)
Prediction
Deep learning
Bidirectional long short-term memory (BiLSTM)
Greedy stepwise feature selection
Friedman test
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954125002006
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http://www.sciencedirect.com/science/article/pii/S1574954125002006

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