NDLI: Uncertainty assessment and optimization of hydrological model with the Shuffled Complex Evolution Metropolis algorithm: an application to artificial neural network rainfall-runoff model

Content Provider	Springer Nature Link
Author	Guo, Jun Zhou, Jianzhong Song, Lixiang Zou, Qiang Zeng, Xiaofan
Copyright Year	2012
Abstract	Assessment of parameter and predictive uncertainty of hydrologic models is an essential part in the field of hydrology. However, during the past decades, research related to hydrologic model uncertainty is mostly done with conceptual models. As is accepted that uncertainty in model predictions arises from measurement errors associated with the system input and output, from model structural errors and from problems with parameter estimation. Unfortunately, non-conceptual models, such as black-box models, also suffer from these problems. In this paper, we take the artificial neural network (ANN) rainfall-runoff model as an example, and the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) is employed to analysis the parameter and predictive uncertainty of this model. Furthermore, based on the results of uncertainty assessment, we finally arrive at a simpler incomplete-connection artificial neural network (ICANN) model as well as with better performance compared to original ANN rainfall-runoff model. These results not only indicate that SCEM-UA can be a useful tool for uncertainty analysis of ANN model, but also prove that uncertainty does exist in ANN rainfall-runoff model. Additionally, in some way, it presents that the ICANN model is with smaller uncertainty than the original ANN model.
Starting Page	985
Ending Page	1004
Page Count	20
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	27
Issue Number	4
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-08-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Uncertainty assessment Hydrological model Artificial neural network Rainfall-runoff model Markov Chain Monte Carlo Math. Application in Environmental Science Earth Sciences Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Computational Intelligence Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Quantifying uncertainty in rainfall–runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia

Spatial risk assessment of typhoon cumulated rainfall: a case study in Taipei area

Uncertainty Propagation of Hydrologic Modeling in Water Supply System Performance: Application of Markov Chain Monte Carlo Method

Copula entropy coupled with artificial neural network for rainfall–runoff simulation

Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Rainfall derivatives pricing with an underlying semi-Markov model for precipitation occurrences

Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Uncertainty assessment and optimization of hydrological model with the Shuffled Complex Evolution Metropolis algorithm: an application to artificial neural network rainfall-runoff model

Similar Documents

Quantifying uncertainty in rainfall–runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia

Spatial risk assessment of typhoon cumulated rainfall: a case study in Taipei area

Uncertainty Propagation of Hydrologic Modeling in Water Supply System Performance: Application of Markov Chain Monte Carlo Method

Copula entropy coupled with artificial neural network for rainfall–runoff simulation

Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Rainfall derivatives pricing with an underlying semi-Markov model for precipitation occurrences

Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: a review

Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management

Uncertainty assessment and optimization of hydrological model with the Shuffled Complex Evolution Metropolis algorithm: an application to artificial neural network rainfall-runoff model