Precipitation's Level Prediction Based on Tree Augmented Naïve Bayes model

At present, most of the precipitation's level predictions use the laws of nature to build the mathematical model which contains one or more series level to carry out the numerical simulation, as thus to analyze the causes and consequences of the evolution. Bayesian model is one kind of the fore...

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Main Authors: Shengjun, Xue (Author), Jingyi, Chen (Author), Xiaolong, Xu (Author), Mengying, Li (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2014-01-01.
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LEADER 02266 am a22003013u 4500
001 ijeecs3008_1017
042 |a dc 
100 1 0 |a Shengjun, Xue  |e author 
100 1 0 |e contributor 
700 1 0 |a Jingyi, Chen  |e author 
700 1 0 |a Xiaolong, Xu  |e author 
700 1 0 |a Mengying, Li  |e author 
245 0 0 |a Precipitation's Level Prediction Based on Tree Augmented Naïve Bayes model 
260 |b Institute of Advanced Engineering and Science,   |c 2014-01-01. 
520 |a At present, most of the precipitation's level predictions use the laws of nature to build the mathematical model which contains one or more series level to carry out the numerical simulation, as thus to analyze the causes and consequences of the evolution. Bayesian model is one kind of the foregoing said. In the Bayesian classification model, Naive Bayes model is known for its stability and easy to operate, but the established precedent assumption tends to be inadmissible. So here the article proposed  a new precipitation's level prediction model based on the tree Augmented Naïve Bayes(we called TAN model for short hereafter), which improve the original Naïve Bayes model defects and increase the association between the leaf nodes on the basis of the original model. And we use the Dongtai station, Jiangsu province meteorology data to test the new precipitation model. The results show that the new precipitation prediction model's performance is superior to the traditional Naive Bayes model. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3997 
540 |a Copyright (c) 2013 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc-nd/4.0 
546 |a eng 
690 |a precipitation; prediction; naïve Bayes; TAN model 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 1: January 2014; 314-322 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v12.i1 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3008/1017 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3008/1017  |z Get fulltext