Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/39159
Title: Investigation internal parameters of neural network model for Flood Forecasting at Upper river Ping, Chiang Mai
Authors: Chaipimonplin,T.
Keywords: Civil and Structural Engineering
Issue Date: 31-Jan-2015
Publisher: Korean Society of Civil Engineers
Abstract: © 2015 Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg The flood issue for forecasters at Chiang Mai derives from the monsoon rainfall, which leads to serious out-of-bank flooding two to four times a year. Data for stage and rainfall per hour in Upper Ping catchment is limited as the historical flood record is limited in length. Neural Network forecasting models are potentially very powerful forecasters where the data are limited. However, insufficient data for Neural Network training reduces the model performance. All data for Neural Network is divided into three datasets: training, validation and testing. In addition most of learning algorithms require validation data unlike Bayesian Regularization (BR) algorithm with no validation data. The power of BR to forecast effectively where data set are limited. Therefore, this algorithm is worth exploring for the Upper Ping catchment, also comparison performance with the Levenberg-Marquardt algorithm (LM) that is the fastest training. In addition, for the best model performance hidden nodes are set as 50%, 75% and 2n+1 of input nodes. The Neural Network model is used to predict water stage at P.1 and P.67 station at lead times of 6 and 12 hours with two different learning algorithms. The results have found that Neural Network performance training with LM algorithm is better than BR algorithms by improving the peak stage. The overall performance of the model that has the hidden nodes less than input nodes of 50% and 75% has the best performance.
URI: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84922366058&origin=inward
http://cmuir.cmu.ac.th/handle/6653943832/39159
ISSN: 12267988
Appears in Collections:SOC: Journal Articles

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