A bootstrap approach for the parameter uncertainty of an urban-specific rainfall-runoff model

Gopalan, Saritha Padiyedath; Kawamura, Akira; Amaguchi, Hideo; Takasaki, Tadakatsu; Azhikodan, Gubash

doi:10.1016/j.jhydrol.2019.124195

Cited by 20 publications

(12 citation statements)

References 66 publications

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“…The uncertainty in the parameter estimations can be reflected in parameter distributions and interactions on account of different optimum parameter sets [6]. Numerous studies are carried out by calibrating the model for different sub periods of all available data to assess the impact of input data on parameter uncertainty Padiyedath Gopalan, et al [7]. Poulin, et al [8] analyzed the hydrological model simulation uncertainty due to climate change impacts on model structure and parameter equality.…”

Section: Uncertainty and Typesmentioning

confidence: 99%

“…For proper planning, design and decision-making process in water resources management, it is predominant to know the total model uncertainty arising from all sources than the individual ones. However, the major concern is which are the sources of uncertainty arising from different data quality, how much confidence bound can be placed on calibration for a given period, how do these affect the simulations and model parameterization [7].…”

Section: Uncertainty and Typesmentioning

confidence: 99%

“…Network (ANN) model, estimating the sampling diversity of reconstructed runoff, designing storms from overrunning series by using non time series data etc [43][44][45]. Nonetheless, bootstrap approach is applying in limited scale to estimate and calibrate parameter uncertainty of rainfall runoff models [7]. Parameter uncertainty associated with forcing data and its impact on streamflow simulations are estimated by nonparametric block bootstrapping approach coupled with global optimization by Ebtehaj, et al [46].…”

Section: This Type Of Replication Technique Is Applied In Variegated ...mentioning

confidence: 99%

“…Evaluation of the impact of different available data scenarios in calibration parameter uncertainty on the model simulation of the discharge in the upper Kanda river basin was done by residual based bootstrap technique in 2019 by Padiyedath Gopalan, et al [7]. The study is based on the assumption of an independent and identically distributed model residual series.…”

Section: This Type Of Replication Technique Is Applied In Variegated ...mentioning

confidence: 99%

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Uncertainty in hydrological modelling: A review

Farzana

2023

ijhr

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Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strengths and weaknesses of the methods in identifying the usefulness of the models. By the comparison of the methods the improvement of the model reliability, slackening of the prediction error of the hydrological models can be suggested. By a proper quantification of uncertainty of data applied for the building up and evaluation of models, model performance can be improved, cost can be reduced and unambiguous results can lead the proper water resources management.

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Section: Uncertainty and Typesmentioning

confidence: 99%

Section: Uncertainty and Typesmentioning

confidence: 99%

Section: This Type Of Replication Technique Is Applied In Variegated ...mentioning

confidence: 99%

Section: This Type Of Replication Technique Is Applied In Variegated ...mentioning

confidence: 99%

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Uncertainty in hydrological modelling: A review

Farzana

2023

ijhr

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“…However, the uncertainty of hybrid models in the estimation process is often neglected in most cases, regarding the fact that the deterministic analysis of hybrid models is the main focus. The bootstrap method coupled with a resampling technique can be an effective method for uncertainty analysis due to the advantages of the more convenient computation process relative to derivatives and the Hessian-matrix involved in the delta method, or the Monte Carlo simulations involved in the Bayesian approach, and has been successfully used in a wide range of problems in hydrological modeling [50][51][52][53]. The applications of combining a machine learning method based on data decomposition technology and a bootstrap resamples method are described in detail in [51,52,54].…”

Section: Introductionmentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

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Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network

Zhang

et al. 2021

Water Resour Manage

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A bootstrap approach for the parameter uncertainty of an urban-specific rainfall-runoff model

Cited by 20 publications

References 66 publications

Uncertainty in hydrological modelling: A review

Uncertainty in hydrological modelling: A review

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network

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