AM-MCMC Algorithm for Runoff Simulation ModelBased on Kernel Density Estimation

doi:10.11988/ckyyb.20160843

Journal of Changjiang River Scientific Research Institute ›› 2018, Vol. 35 ›› Issue (1): 36-39.DOI: 10.11988/ckyyb.20160843

• WATER RESOURCES AND ENVIRONMENT • Previous Articles Next Articles

AM-MCMC Algorithm for Runoff Simulation ModelBased on Kernel Density Estimation

TONG Kun^1,2, LIU Heng¹, GENG Lei-hua¹, XU Peng-bo¹

1.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic ResearchInstitute, Nanjing 210029, China;
2.Key Laboratory of Watershed Geographic Science, Nanjing Institute ofGeography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

Received:2016-08-17 Published:2018-01-01 Online:2018-01-01

Abstract

Abstract: The simulation of runoff probability in an area in lack of runoff data is a difficulty in hydrological research. In this article, we try to establish the probability density function of monthly runoff flow by adopting kernal density estimation method, and give the solution by Markov Chain Monte Carlo (MCMC) simulation method based on Adaptive Metropolis (AM) algorithm. Case study shows that the AM-MCMC algorithm model based on kernel density estimation is of high accuracy and good application value. It can be used in areas in lack of data.

Key words: runoff simulation, probability distribution, kernel density estimation, AM-MCMC algorithm, Luo’ao Reservoir

CLC Number:

TV214

TONG Kun, LIU Heng, GENG Lei-hua, XU Peng-bo. AM-MCMC Algorithm for Runoff Simulation ModelBased on Kernel Density Estimation[J]. Journal of Changjiang River Scientific Research Institute, 2018, 35(1): 36-39.

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AM-MCMC Algorithm for Runoff Simulation ModelBased on Kernel Density Estimation

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