Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

Abstract. Monte-Carlo (MC) simulation based techniques are often applied for the estimation of uncertainties in hydrological models due to uncertain parameters. One such technique is the Generalised Likelihood Uncertainty Estimation technique (GLUE). A major disadvantage of MC is the large number of runs required to establish a reliable estimate of model uncertainties. To reduce the number of runs required, a hybrid genetic algorithm and artificial neural network, known as GAANN, is applied. In this method, GA is used to identify the area of importance and ANN is used to obtain an initial estimate of the model performance by mapping the response surface. Parameter sets which give non-behavioural model runs are discarded before running the hydrological model, effectively reducing the number of actual model runs performed. The proposed method is applied to the case of a simple two-parameter model where the exact parameters are known as well as to a widely used catchment model where the parameters are to be estimated. The results of both applications indicated that the proposed method is more efficient and effective, thereby requiring fewer model simulations than GLUE. The proposed method increased the feasibility of applying uncertainty analysis to computationally intensive simulation models. Keywords: parameters, calibration, GLUE, Monte-Carlo simulation, Genetic Algorithms, Artificial Neural Networks, hydrological modelling, Singapore

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Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.2.59 ◽

2020 ◽

Vol 55 (2) ◽

Author(s):

Rafid Abbas Ali ◽

Faten Sajet Mater ◽

Asmaa Satar Jeeiad Al-Ragehey

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Network Architecture ◽

Vapor Concentration ◽

Neural Network Architecture ◽

Marquardt Algorithm ◽

Artificial Neural ◽

New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

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A Comparison of Traditional Segmentation Methods with Segmentation Based upon Artificial Neural Networks by Means of Conjoint Data from a Monte-Carlo-Simulation

Classification, Data Analysis, and Data Highways - Studies in Classification, Data Analysis, and Knowledge Organization ◽

10.1007/978-3-642-72087-1_41 ◽

1998 ◽

pp. 385-392 ◽

Cited By ~ 2

Author(s):

H. Gierl ◽

S. Schwanenberg

Keyword(s):

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Segmentation Methods ◽

Artificial Neural

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Monte Carlo Simulation Models

Designing High Availability Systems ◽

10.1002/9781118739853.ch17 ◽

2013 ◽

pp. 340-366

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Models

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The Use of Quantitative Methods in Investment Decisions

Handbook of Research on Global Issues in Financial Communication and Investment Decision Making - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-9265-5.ch013 ◽

2019 ◽

pp. 256-275 ◽

Cited By ~ 5

Author(s):

Serkan Eti

Keyword(s):

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Decision Tree ◽

Quantitative Methods ◽

Simulation Analysis ◽

Investment Decision ◽

Tree Algorithms ◽

Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

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