Time Lagged Back Propagation Neural Network with rainfall for flood forecasting

Conference proceedings article

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Publication Details

Author list: Lueangaram S., Waraporn N.

Publisher: Hindawi

Publication year: 2016

Start page: 63

End page: 68

Number of pages: 6

ISBN: 9781509012169

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987950732&doi=10.1109%2fINES.2016.7555094&partnerID=40&md5=43242053439d0a980150b41cdeafafd7

Languages: English-Great Britain (EN-GB)

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Abstract

Flooding is a major problem in many countries while flood models have been proposed by various research groups on hydrological model in the past. Recently, Artificial Neural Network has been applied to the flood model. However, the learning factors of Neural Network for flood models need to include water level and rainfall which are two major impacts to the flooding. Therefore, this paper proposed Time-Lagged Back Propagation Neural Network Model with water flow and rainfall for flood forecasting. We ran our model with water flow and rainfall at the rivers around the lower part of northern region of Thailand. We compared the results between Back Propagation Neural Network Model and Time Lagged Back Propagation Neural Network Model with Gamma Memory. The study shows that Time Lagged Back Propagation Neural Network Model with Gamma Memory has better performance. ฉ 2016 IEEE.

Keywords

Flood Prediction, Time-Lagged Back Propagation Neural Network