A river network model using a weight-based merged LSTM for multi-source monitoring integration
Rivers typically exhibit spatial connectivity from upstream to downstream, with various heterogeneous monitoring systems operating concurrently across basins. While graph neural networks (GNNs) have shown promise in modeling spatial connectivity, they remain limited by reliance on features common to...
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| Format: | Article |
| Language: | English |
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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/S1574954125003292 |
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| author | Jonggyu Jung Taeseung Park Jaegwan Park Dogeon Lee YoonKyung Cha |
| author_facet | Jonggyu Jung Taeseung Park Jaegwan Park Dogeon Lee YoonKyung Cha |
| author_sort | Jonggyu Jung |
| collection | DOAJ |
| description | Rivers typically exhibit spatial connectivity from upstream to downstream, with various heterogeneous monitoring systems operating concurrently across basins. While graph neural networks (GNNs) have shown promise in modeling spatial connectivity, they remain limited by reliance on features common to all nodes. As the diversity of monitoring systems increases, the overlap of measured variables decreases, reducing usable input features and limiting model applicability. To address this issue, this study proposes a river network model based on a weight-based merged long short-term memory (LSTM) architecture for forecasting daily total organic carbon (TOC) concentrations by integrating multi-source data from spatially connected monitoring sites. The river network is divided into upstream, midstream, and downstream segments, with each input processed independently and merged into a unified representation. Segment models are connected in sequence to represent directional flow. Trainable scalar weights are included to quantify the relative contribution of each site and enhance spatial interpretability. The model is applied to a section of the Han River in South Korea, which flows through the Seoul metropolitan area in South Korea. The model demonstrates strong forecasting performance, with mean absolute errors ranging from 0.055 to 0.518, root mean squared errors from 0.075 to 0.784, and coefficients of determination between 0.424 and 0.721. Scenario analyses using site-specific contributions are conducted to evaluate changes in TOC concentrations under pollution reduction scenarios. This river network modeling framework is adaptable to a wide range of applications and provides practical utility for watershed-scale water quality forecasting and management. |
| format | Article |
| id | doaj-art-c6e9d2f540af417899ccbb6707e1ecbb |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-c6e9d2f540af417899ccbb6707e1ecbb2025-08-20T05:05:45ZengElsevierEcological Informatics1574-95412025-12-019010332010.1016/j.ecoinf.2025.103320A river network model using a weight-based merged LSTM for multi-source monitoring integrationJonggyu Jung0Taeseung Park1Jaegwan Park2Dogeon Lee3YoonKyung Cha4School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of KoreaSchool of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of KoreaSchool of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of KoreaSchool of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of KoreaCorresponding author.; School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of KoreaRivers typically exhibit spatial connectivity from upstream to downstream, with various heterogeneous monitoring systems operating concurrently across basins. While graph neural networks (GNNs) have shown promise in modeling spatial connectivity, they remain limited by reliance on features common to all nodes. As the diversity of monitoring systems increases, the overlap of measured variables decreases, reducing usable input features and limiting model applicability. To address this issue, this study proposes a river network model based on a weight-based merged long short-term memory (LSTM) architecture for forecasting daily total organic carbon (TOC) concentrations by integrating multi-source data from spatially connected monitoring sites. The river network is divided into upstream, midstream, and downstream segments, with each input processed independently and merged into a unified representation. Segment models are connected in sequence to represent directional flow. Trainable scalar weights are included to quantify the relative contribution of each site and enhance spatial interpretability. The model is applied to a section of the Han River in South Korea, which flows through the Seoul metropolitan area in South Korea. The model demonstrates strong forecasting performance, with mean absolute errors ranging from 0.055 to 0.518, root mean squared errors from 0.075 to 0.784, and coefficients of determination between 0.424 and 0.721. Scenario analyses using site-specific contributions are conducted to evaluate changes in TOC concentrations under pollution reduction scenarios. This river network modeling framework is adaptable to a wide range of applications and provides practical utility for watershed-scale water quality forecasting and management.http://www.sciencedirect.com/science/article/pii/S1574954125003292River networkWeight-based merged LSTMSpatial connectivityFeature-flexibilityWater quality forecastingTotal organic carbon |
| spellingShingle | Jonggyu Jung Taeseung Park Jaegwan Park Dogeon Lee YoonKyung Cha A river network model using a weight-based merged LSTM for multi-source monitoring integration Ecological Informatics River network Weight-based merged LSTM Spatial connectivity Feature-flexibility Water quality forecasting Total organic carbon |
| title | A river network model using a weight-based merged LSTM for multi-source monitoring integration |
| title_full | A river network model using a weight-based merged LSTM for multi-source monitoring integration |
| title_fullStr | A river network model using a weight-based merged LSTM for multi-source monitoring integration |
| title_full_unstemmed | A river network model using a weight-based merged LSTM for multi-source monitoring integration |
| title_short | A river network model using a weight-based merged LSTM for multi-source monitoring integration |
| title_sort | river network model using a weight based merged lstm for multi source monitoring integration |
| topic | River network Weight-based merged LSTM Spatial connectivity Feature-flexibility Water quality forecasting Total organic carbon |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003292 |
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