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Analysis of the applicability of parameter estimation methods for a stochastic rainfall generation model
Journal of the Korean Data & Information Science Society 2017;28:1447-56
Published online November 30, 2017
© 2017 Korean Data & Information Science Society.

Hyungon Cho1 · Kyeong Eun Lee2 · Gwangseob Kim3

13School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National University
2Department of Statistics, Kyungpook National University
Correspondence to: Gwangseob Kim
Professor, School of Architectural, Civil, Environment, and Energy Engineering, Kyungpook National University, Daegu 41566, Korea. E-mail: kimgs@knu.ac.kr
Received October 30, 2017; Revised November 16, 2017; Accepted November 21, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.
Keywords : Davidon-Fletcher-Powell, differential evolution, genetic algorithm, Nelder-Mead, parameter estimation, stochastic rainfall generation model