New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model
•New paradigm was used for development of watershed model.•Machine learning algorithm and mechanistic model are effectively coupled.•Equations for best management practices evaluation were established.•The paradigm shows higher interpretability based on mechanistic model. The development of watershe...
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| Published in: | Journal of hydrology (Amsterdam) Vol. 636; p. 131264 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.06.2024
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| ISSN: | 0022-1694, 1879-2707 |
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| Abstract | •New paradigm was used for development of watershed model.•Machine learning algorithm and mechanistic model are effectively coupled.•Equations for best management practices evaluation were established.•The paradigm shows higher interpretability based on mechanistic model.
The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models. |
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| AbstractList | •New paradigm was used for development of watershed model.•Machine learning algorithm and mechanistic model are effectively coupled.•Equations for best management practices evaluation were established.•The paradigm shows higher interpretability based on mechanistic model.
The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models. The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models. |
| ArticleNumber | 131264 |
| Author | Shen, Zhenyao Liu, Guowangchen Wang, Shuai Zhu, Kaihang Wang, Wenzhuo Chen, Lei |
| Author_xml | – sequence: 1 givenname: Guowangchen surname: Liu fullname: Liu, Guowangchen – sequence: 2 givenname: Lei orcidid: 0000-0001-8415-3896 surname: Chen fullname: Chen, Lei email: chenlei1982bnu@bnu.edu.cn – sequence: 3 givenname: Wenzhuo surname: Wang fullname: Wang, Wenzhuo – sequence: 4 givenname: Shuai surname: Wang fullname: Wang, Shuai – sequence: 5 givenname: Kaihang surname: Zhu fullname: Zhu, Kaihang – sequence: 6 givenname: Zhenyao orcidid: 0000-0002-6620-1943 surname: Shen fullname: Shen, Zhenyao |
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| Cites_doi | 10.1016/j.watres.2023.119991 10.1016/j.watres.2022.118721 10.1016/j.jenvman.2011.10.006 10.1016/j.jhydrol.2004.09.005 10.1029/2007WR006749 10.1016/j.jhydrol.2011.01.004 10.1016/j.scitotenv.2020.138091 10.3390/rs12030475 10.1016/j.envsoft.2019.104602 10.1016/j.scitotenv.2020.138600 10.1126/science.aaa8415 10.1016/j.jocs.2013.07.004 10.1016/j.scitotenv.2022.156643 10.2166/wst.2022.115 10.1016/j.jenvman.2021.114411 10.1016/j.jhydrol.2019.124133 10.1021/acs.est.9b07511 10.3390/su10020432 10.1016/j.chemosphere.2022.133875 10.1016/j.jenvman.2022.116491 10.1016/j.jhydrol.2020.124875 10.1016/j.advwatres.2012.09.001 10.1016/j.envsoft.2019.104529 10.1016/j.jhydrol.2010.06.033 10.1016/j.jhydrol.2019.06.073 10.1016/j.watres.2022.119028 10.3390/su11195426 10.1016/j.catena.2021.105178 10.1038/s41561-021-00889-9 |
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| Keywords | Best management practices (BMPs) Machine learning (ML) Multilayer perceptron regression (MLPR) Watershed model Nonpoint source (NPS) pollution |
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| SubjectTerms | algorithms Best management practices (BMPs) biomass case studies China data collection hydrologic models lakes Machine learning (ML) mechanistic models Multilayer perceptron regression (MLPR) neural networks Nonpoint source (NPS) pollution pollution regression analysis soil types vegetation Watershed model watersheds |
| Title | New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model |
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