An association rule mining approach in predicting flood areas
This study focuses on the application of Association rules mining for the flood data in Terengganu. Flood is one of the natural disasters that happens every year during the monsoon season and causes damage towards people, infrastructure and the environment. This paper aimed to find the correlation b...
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my-unisza-ir.16872020-11-19T07:32:26Z http://eprints.unisza.edu.my/1687/ An association rule mining approach in predicting flood areas Mokhairi, Makhtar Nur Ashikin, Harun Azwa, Abdul Aziz Zahrahtul Amani, Zakaria Engku Fadzli Hasan, Syed Abdullah Julaily Aida, Jusoh QA75 Electronic computers. Computer science This study focuses on the application of Association rules mining for the flood data in Terengganu. Flood is one of the natural disasters that happens every year during the monsoon season and causes damage towards people, infrastructure and the environment. This paper aimed to find the correlation between water level and flood area in developing a model to predict flood. Malaysian Drainage and Irrigation Department supplied the dataset which were the flood area, water level and rainfall data. The association rules mining technique will generate the best rules from the dataset by using Apriori algorithm which had been applied to find the frequent itemsets. Consequently, by using the Apriori algorithm, it generated the 10 best rules with 100% confidence level and 40% minimum support after the candidate generation and pruning technique. The results of this research showed the usability of data mining in this field and can help to give early warning towards potential victims and spare some time in saving lives and properties. 2017 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/1687/1/FH03-FIK-17-08103.jpg Mokhairi, Makhtar and Nur Ashikin, Harun and Azwa, Abdul Aziz and Zahrahtul Amani, Zakaria and Engku Fadzli Hasan, Syed Abdullah and Julaily Aida, Jusoh (2017) An association rule mining approach in predicting flood areas. In: The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016; Bandung; Indonesia, 18-20 August 2016, Bandung; Indonesia. |
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QA75 Electronic computers. Computer science Mokhairi, Makhtar Nur Ashikin, Harun Azwa, Abdul Aziz Zahrahtul Amani, Zakaria Engku Fadzli Hasan, Syed Abdullah Julaily Aida, Jusoh An association rule mining approach in predicting flood areas |
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This study focuses on the application of Association rules mining for the flood data in Terengganu. Flood is one of the natural disasters that happens every year during the monsoon season and causes damage towards people, infrastructure and the environment. This paper aimed to find the correlation between water level and flood area in developing a model to predict flood. Malaysian Drainage and Irrigation Department supplied the dataset which were the flood area, water level and rainfall data. The association rules mining technique will generate the best rules from the dataset by using Apriori algorithm which had been applied to find the frequent itemsets. Consequently, by using the Apriori algorithm, it generated the 10 best rules with 100% confidence level and 40% minimum support after the candidate generation and pruning technique. The results of this research showed the usability of data mining in this field and can help to give early warning towards potential victims and spare some time in saving lives and properties. |
format |
Conference or Workshop Item |
author |
Mokhairi, Makhtar Nur Ashikin, Harun Azwa, Abdul Aziz Zahrahtul Amani, Zakaria Engku Fadzli Hasan, Syed Abdullah Julaily Aida, Jusoh |
author_facet |
Mokhairi, Makhtar Nur Ashikin, Harun Azwa, Abdul Aziz Zahrahtul Amani, Zakaria Engku Fadzli Hasan, Syed Abdullah Julaily Aida, Jusoh |
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Mokhairi, Makhtar |
title |
An association rule mining approach in predicting flood areas |
title_short |
An association rule mining approach in predicting flood areas |
title_full |
An association rule mining approach in predicting flood areas |
title_fullStr |
An association rule mining approach in predicting flood areas |
title_full_unstemmed |
An association rule mining approach in predicting flood areas |
title_sort |
association rule mining approach in predicting flood areas |
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2017 |
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http://eprints.unisza.edu.my/1687/1/FH03-FIK-17-08103.jpg http://eprints.unisza.edu.my/1687/ |
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1684657737419456512 |