Nonparametric Kernel estimation of annual maximum stream flow quantiles.

• Main Author: Shabri, Ani
• Format: Article
• Language: English
• Published: Department of Mathematics, Faculty of Science 2002
• Subjects: QA Mathematics
• Online Access: http://eprints.utm.my/id/eprint/8811/1/AniShabri2002_NonparametricKernelEstimationofAnnual.pdf; http://eprints.utm.my/id/eprint/8811/; http://portal.psz.utm.my/psz/index.php?option=com_content&task=view&id=128&Itemid=305&PHPSESSID=81b664e998055f65b4ccff8f61bf7cb2
• Institution: Universiti Teknologi Malaysia

A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean square error and root mean absolute error. Based on correlation coefficient test shown that the nonparametric model approach is accurate, uniform and flexible alternatives to parametric models for flood frequency analysis.