IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 32nd Congress - Venice (2007) : THEME A: Engineering and Management of Fresh-water Systems : Estimating missing values of daily rainfall using classification techniques
Estimating missing values of daily rainfall using classification techniques
Author : Tae-Woong Kim, Hosung Ahn, Jae-Hyun Ahn, Sung-Ho Byeon, Sung-Wook Wi, and Moonil Kim
Missing data in daily rainfall records need to be filled in accurately beforehand. Presented herein is an effort to develop a new spatial daily rainfall model that is intended specifically to fill in gaps in the measured rainfalls. This study adopted a neural networkoriented pattern classifier that determines a daily rainfall occurrence. We herein tested four alternative classifiers. The testing results reveal that a probabilistic neural network approach was superior to the others. Also, a stepwise regression performed better for estimating rainfall amounts than other competing approaches. This study proved that the proposed model produced accurate and unbiased estimates for missing values of daily rainfall.
File Size : 654,898 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 32nd Congress - Venice (2007)
Article : THEME A: Engineering and Management of Fresh-water Systems
Date Published : 01/07/2007
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