Open AccessParameter Optimization, Uncertainty Estimation and Sensitivity Analysis in Hydrological Modeling
Author(s) -
Rajesh Vijaykumar Kherde,
Priyadarshi H. Sawant
Publication year - 2018
Publication title -
european journal of engineering research and science
Language(s) - English
Resource type - Journals
ISSN - 2506-8016
DOI - 10.24018/ejers.2018.3.11.907
Subject(s) - equifinality , monte carlo method , parameter space , sensitivity (control systems) , calibration , estimation theory , set (abstract data type) , local optimum , mathematics , mathematical optimization , function (biology) , statistical physics , statistics , computer science , physics , electronic engineering , artificial intelligence , evolutionary biology , engineering , biology , programming language
This paper describes the application of Monte-Carlo simulations for parameter optimization, uncertainty estimation and sensitivity analysis using hydrological model developed by author [8] for Wardha River basin, Maharashtra, India. The Monte Carlo simulations revealed that the average values of parameters for the local optima of the calibration period seem to give good fit to the data and performance measure (NSE) does not differ significantly from the local optima of the respective calibration years. It is interesting to notice that, if the Monte Carlo simulations are carried out all over again, it generate yet another set of random numbers as realizations of model parameters. However the model objective function (NSE) differs mere by 0.1% by running the new set of realizations and the local optimum parameter values are close to the earlier local optima. It seems that the model structure is in agreement with the ‘‘equifinality’’ or ‘‘non-uniqueness’’ concept as many different parameter sets give good fit to the data. However particular area of the parameter space is observed to be dominant in fitting the available observations, this is in contradiction to Beven’s theory behind rejecting the idea of optimum parameter set.