Prediction of Annual Nitrate-N Losses in Drain Outflows With Artificial Neural Networks

Salehi, Fakhreddin; Prasher, Shiv O.; Amin, Sahar A.; Madani, Ali; Jebelli, S. J.; Ramaswamy, H. S.; Tan, C. S.; Drury, C. F.

doi:10.13031/2013.3006

Cited by 23 publications

(16 citation statements)

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“…Those SA methods have been widely used for land surface and hydrologic models (e.g., Salehi et al 2000;Cullmann et al 2006;Demaria et al 2007;Tang et al 2007;Pappenberger et al 2008;Hall et al 2009;Reusser et al 2011;Massmann and Holzmann 2012). The comparison of SA methods has been the interest of many studies (e.g., Saltelli et al 2005;Frey and Patil 2002;Hall et al 2009;Confalonieri et al 2010;Reusser et al 2011;Massmann and Holzmann 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Shi

Davis

Zhang

et al. 2014

Journal of Hydrometeorology

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Land surface models (LSMs) and hydrologic models are parameterized models. The number of involved parameters is often large. Sensitivity analysis (SA) is a key step to understand the complex relationships between parameters and between state variables and parameters. SA is also critical to understand system dynamics and to examine the parameter identifiability. In this paper, parameter SA for a fully coupled, physically based, distributed land surface hydrologic model, namely, the Flux-Penn State Integrated Hydrologic Model (Flux-PIHM), is performed. Multiparameter and single-parameter tests are performed to examine the three dimensions of identifiability: distinguishability, observability, and simplicity. Results show that Flux-PIHM model predictions of discharge, water table depth, soil moisture, land surface temperature, and surface heat fluxes are very sensitive to the selection of parameter values. Parameter uncertainties produce large uncertainties in hydrologic and land surface variable predictions. The van Genuchten parameters a and b and the Zilitinkevich parameter C zil are the most identifiable among the 20 tested parameters. Results indicate that the land surface and the subsurface are closely coupled. Hydrologic parameters have significant influence on land surface simulations. At the same time, land surface parameters have considerable impacts on hydrologic simulations; the evapotranspiration prediction prior to a strong precipitation event is critical for initializing accurate prediction of discharge peaks. Results also show that parameter identifiability depends on seasons and canopy wetness. Parameter identifiability at high and low flow conditions can be extremely different. Complex system dynamics have been revealed during the SA.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Shi

Davis

Zhang

et al. 2014

Journal of Hydrometeorology

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show abstract

“…One use of nominal sensitivity analysis is as a screening analysis to identify the most important inputs to propagate through a model in a probabilistic framework (Cullen and Frey, 1999). Nominal range sensitivity can be used to prioritize data collection needs as demonstrated by Salehi et al . (2000).…”

Section: Sensitivity Analysis Methodsmentioning

confidence: 99%

“…Mathematical methods assess sensitivity of a model output to the range of variation of an input. These methods typically involve calculating the output for a few values of an input that represent the possible range of the input (e.g., Salehi et al ., 2000). These methods do not address the variance in the output due to the variance in the inputs, but they can assess the impact of range of variation in the input values on the output (Morgan and Henrion, 1990).…”

Section: Introductionmentioning

confidence: 99%

Identification and Review of Sensitivity Analysis Methods

Frey¹,

Patil²

2002

Risk Analysis

703

114

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Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.

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“…These techniques to conduct sensitivity analysis can be classified in variety of ways. Most of the classifications schemes focus on the capability, rather than the methodology, of a specific technique (Salehi et al 2000). Based on the methodology, sensitivity analysis can be classified as mathematical, statistical, or graphical techniques (Frey and Patil 2002).…”

Section: Simulation-based Localized Sensitivity Analyses (Salsa)mentioning

confidence: 99%

Simulation-Based Localized Sensitivity Analyses (SaLSA) — An Example of Water Quality Failures in Distribution Networks

Sadiq¹,

Kleiner²,

Rajani³

2008

World Environmental and Water Resources Congress 2008

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The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42

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Prediction of Annual Nitrate-N Losses in Drain Outflows With Artificial Neural Networks

Cited by 23 publications

References 0 publications

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Evaluation of the Parameter Sensitivities of a Coupled Land Surface Hydrologic Model at a Critical Zone Observatory

Identification and Review of Sensitivity Analysis Methods

Simulation-Based Localized Sensitivity Analyses (SaLSA) — An Example of Water Quality Failures in Distribution Networks

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