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1860797838263320576
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| building |
INTELEK Repository
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| collection |
Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2024-08-27 11:02:14
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| format |
Restricted Document
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| id |
14331
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UniSZA
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3336-01-FH05-FIK-17-07718.pdf
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Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/95.0.4638.69 Safari/537.36
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14331
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| spelling |
14331 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=14331 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Book Chapter application/pdf 2 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML like Gecko) Chrome/95.0.4638.69 Safari/537.36 2024-08-27 11:02:14 3336-01-FH05-FIK-17-07718.pdf 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. Springer International Publishing AG Springer International Publishing AG 437-446 Recent Advances on Soft Computing and Data Mining
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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
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| title_fullStr |
An Association Rule Mining Approach in Predicting Flood Areas
|
| title_full_unstemmed |
An Association Rule Mining Approach in Predicting Flood Areas
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| 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
|