| _version_ |
1860799669960966144
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2017-02-02 11:54:15
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| eventvenue |
Bandung, Indonesia
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| format |
Restricted Document
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| id |
6929
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| institution |
UniSZA
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| originalfilename |
1687-01-FH03-FIK-17-08103.jpg
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| person |
norman
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6929
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| spelling |
6929 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6929 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper image/jpeg inches 96 96 norman 771 1436 2017-02-02 11:54:15 1436x771 78 78 1687-01-FH03-FIK-17-08103.jpg UniSZA Private Access 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 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. The 2nd International Conference on Soft Computing and Data Mining, SCDM-2016; Bandung; Indonesia Bandung, Indonesia
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| spellingShingle |
An association rule mining approach in predicting flood areas
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| summary |
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.
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| title |
An association rule mining approach in predicting flood areas
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| title_full |
An association rule mining approach in predicting flood areas
|
| title_fullStr |
An association rule mining approach in predicting flood areas
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| title_full_unstemmed |
An association rule mining approach in predicting flood areas
|
| title_short |
An association rule mining approach in predicting flood areas
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| title_sort |
association rule mining approach in predicting flood areas
|