HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network
Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduc...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000032 |
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| author | Tam V. Nguyen Vinh Ngoc Tran Hoang Tran Doan Van Binh Toan D. Duong Thanh Duc Dang Pia Ebeling |
| author_facet | Tam V. Nguyen Vinh Ngoc Tran Hoang Tran Doan Van Binh Toan D. Duong Thanh Duc Dang Pia Ebeling |
| author_sort | Tam V. Nguyen |
| collection | DOAJ |
| description | Machine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills. |
| format | Article |
| id | doaj-art-4e55e25520264cf490c7d5c9a5fe4138 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-4e55e25520264cf490c7d5c9a5fe41382025-01-19T06:24:44ZengElsevierEcological Informatics1574-95412025-03-0185102994HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural networkTam V. Nguyen0Vinh Ngoc Tran1Hoang Tran2Doan Van Binh3Toan D. Duong4Thanh Duc Dang5Pia Ebeling6Department of Hydrogeology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany; Corresponding author.Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USAAtmospheric Science & Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USAFaculty of Engineering, Vietnamese-German University, Ben Cat Town, Binh Duong Province 820000, Viet NamThuyloi University, 175 Tay Son, Dong Da, Hanoi, Viet NamNational Institute of Water and Atmospheric Research, Auckland, New ZealandDepartment of Hydrogeology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, GermanyMachine learning (ML) is emerging as a promising tool for modeling hydro-ecological processes due to the increasing availability of large environmental data. However, the use of ML requires sufficient programming knowledge due to a lack of a graphical user interface (GUI). In this study, we introduced a GUI package, named HydroEcoLSTM, with the long short-term memory network (LSTM) as the core model, that allows non-ML experts to utilize their domain knowledge to construct complex ML models. We demonstrated the functionalities of HydroEcoLSTM with two practical examples, including (1) predictions of streamflow in both gauged and ungauged catchments and (2) predictions of multiple outputs (i.e., streamflow and isotope transport from two catchments). The simulation results obtained in both case experiments are satisfactory. In the first example, the average Nash–Sutcliffe Efficiency (NSE) for streamflow simulation during the testing period is 0.79 while the application of the trained model in two assumed ungauged catchments also achieves the average NSE of 0.68. In the second example, the average NSE for streamflow and instream isotope simulation during the testing period is 0.71. Ultimately, applications of HydroEcoLSTM with real-world examples demonstrate its potential use for practical applications and research without requiring extensive coding skills.http://www.sciencedirect.com/science/article/pii/S1574954125000032Machine learningLong short-term memoryEcological modelingHydrological modeling |
| spellingShingle | Tam V. Nguyen Vinh Ngoc Tran Hoang Tran Doan Van Binh Toan D. Duong Thanh Duc Dang Pia Ebeling HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network Ecological Informatics Machine learning Long short-term memory Ecological modeling Hydrological modeling |
| title | HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network |
| title_full | HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network |
| title_fullStr | HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network |
| title_full_unstemmed | HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network |
| title_short | HydroEcoLSTM: A Python package with graphical user interface for hydro-ecological modeling with long short-term memory neural network |
| title_sort | hydroecolstm a python package with graphical user interface for hydro ecological modeling with long short term memory neural network |
| topic | Machine learning Long short-term memory Ecological modeling Hydrological modeling |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000032 |
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