Abstract
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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Acknowledgements
The presented work was funded by the Ministry of Higher Education Malaysia under the Research Acculturation Grant Scheme (RAGS) reference code RR095 and UniSZA. The authors would like to thank the Malaysian Drainage and Irrigation Department for supplying the data of flood in Terengganu and to all those who had participated in this research.
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Makhtar, M., Harun, N.A., Aziz, A.A., Zakaria, Z.A., Abdullah, F.S., Jusoh, J.A. (2017). An Association Rule Mining Approach in Predicting Flood Areas. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_44
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DOI: https://doi.org/10.1007/978-3-319-51281-5_44
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