Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models Article Swipe
K. S. Kasiviswanathan
,
K. P. Sudheer
,
Jianxun He
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1080/02626667.2017.1393686
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1080/02626667.2017.1393686
Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/02626667.2017.1393686
- https://www.tandfonline.com/doi/pdf/10.1080/02626667.2017.1393686?needAccess=true
- OA Status
- bronze
- Cited By
- 18
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2766193180
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https://openalex.org/W2766193180Canonical identifier for this work in OpenAlex
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https://doi.org/10.1080/02626667.2017.1393686Digital Object Identifier
- Title
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Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological modelsWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-10-17Full publication date if available
- Authors
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K. S. Kasiviswanathan, K. P. Sudheer, Jianxun HeList of authors in order
- Landing page
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https://doi.org/10.1080/02626667.2017.1393686Publisher landing page
- PDF URL
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https://www.tandfonline.com/doi/pdf/10.1080/02626667.2017.1393686?needAccess=trueDirect link to full text PDF
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bronzeOpen access status per OpenAlex
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https://www.tandfonline.com/doi/pdf/10.1080/02626667.2017.1393686?needAccess=trueDirect OA link when available
- Concepts
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Uncertainty quantification, Probabilistic logic, Computer science, Artificial neural network, Uncertainty analysis, Identification (biology), Sensitivity analysis, Ensemble forecasting, Machine learning, Hydrological modelling, Artificial intelligence, Data mining, Simulation, Botany, Geology, Climatology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 1, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2064184542, https://openalex.org/W2065902166, https://openalex.org/W2126105956, https://openalex.org/W1530052729, https://openalex.org/W2023412732, https://openalex.org/W2031292142, https://openalex.org/W2083011292, https://openalex.org/W1574940769, https://openalex.org/W4233948460, https://openalex.org/W1978128908, https://openalex.org/W1975271094, https://openalex.org/W2095239580, https://openalex.org/W2112435054, https://openalex.org/W2083599479, https://openalex.org/W1944668482, https://openalex.org/W1978952216, https://openalex.org/W1981320000, https://openalex.org/W1619499024, https://openalex.org/W2009203913, https://openalex.org/W2053934894, https://openalex.org/W2126171847, https://openalex.org/W1909649765, https://openalex.org/W2146495904, https://openalex.org/W2114291377, https://openalex.org/W2156836859, https://openalex.org/W2057323211, https://openalex.org/W2147354624, https://openalex.org/W2028013207, https://openalex.org/W1998275926, https://openalex.org/W1994424196 |
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