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1860797525350416384
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-06-16 11:45:15
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Restricted Document
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13095
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UniSZA
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[1] Paulo Cortez and Anibal Morais, “A Data Mining Approach to Predict Forest Fires using Meteorological Data”, 2007. [2] Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth, “Knowledge Discovery and Data Mining : Towards a Unifying Framework”, Proceedings of KDD-9,1996, pp.82-88. [3] Li Zheng,Chao Sheng, Liang Tang, Tao Li, Steve Luis and Shu-Ching Chen, “Applying Data Mining Techniques to Address Disaster Information Management Challenges on Mobile Devices,” Proceedings of KDD1,August 21-24, 2011, pp. 283-291. [4] Milan Cisty and Juraj Bezak, “The Application of Data Mining Methods for Short Time Flows Prediction in Flood Warning Systems,” Recent Advances in Continuum Mechanics, Hydrology and Ecology, 2013, pp. 92-97. [5] Ku Ruhana Ku,Mahamud, Norhayani Zakaria, Norliza Katuk, and Mohamad Shbier, “Flood Pattern Detection Using Sliding Window Technique,” Third Asia International Conference on Modelling & Simulation, 2009, pp. 45-50. [6] Giorgio Corani and Giorgio Guariso, “Coupling Fuzzy Modelling and Neural Networks for River Flood Prediction,” IEEE Transaction on Systems,Man,and Cybernetics-Part C:Applications and Reviews, Vol. 35, No 3, August 2005. [7] Fazlina A.R, Abd Manan S., Zainazlan M.Z. and Ramli Adnan, “Flood Water Level Modeling and Prediction Using NARX Neural Network: Case Study at Kelang River,” IEEE 10th International Colloqium on Signal Processing & its Application (CSPA2014), Mac 7-9, 2014, pp. 204-207. [8] Darka Mioc, Francois Anton and Brandford George Nickerson, “Decision Support for Flood Event Prediction and Monitoring,” 2007. [9] Yan Li and Manchun Li, “Application and Research on Flood Risk Assessment Decision Support System in the Lower Yellow River,” 2011. [10] Omar Al-Azzam, Deli Sarsar, Kirubel Seifu, Mehdi Mekni, “Flood prediction and Risk Assesment Using Advanced GeoVisualization and Data Mining Technique : A case study in the Red-Lake Valley,” Proceedings of Applied Computational Science, 2014. [11] Masond Bakhtyari Kia, Saled Piratesh, Biswajeet Pradhan, Ahmad Rodzi Mahmud, Wan Nor Azmin Sulaiman, Abbas Moradi, “An artificial neural network model for flood simulation using GIS : Johor River Basin, Malaysia,” Environ Earth Sci, Springer articles, December 31, 2011. [12] Daniela Stonajova, Pance Panov, Andrej Kobler, Saso Dzeroski and Katerina Taskova, “Learning to Predict Forest Fires With Different Data Mining Techniques,” 2006. [13] Ch. Lucas, St. Werder and H.-P. Bahr, “Information Mining for Disaster Management,” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (3/W49), 2007, pp. 75-80. [14] C.T.Dhanya and D.Nagesh Kumar.Fuzzy Association Rules for Prediction of Monsoon Rainfall,” 4th Indian International Conference on Artificial Intelligence (IICAI-09), 2009, pp. 1299-1309. [15] Thomas Landssdall-Welfare, Seatviga Sudjkar, et. Al, “On the Coverage of Science in the Media : A Big Data Study on the Impact of the Fukishima Disaster,” IEEE International Conference on Big Data, 2014, pp.60-66. [16] Qiang Yang.2006.10 Challenging Problems in Data Mining Research. [17] Mary McGlohon ,”Data Mining Disasters: a report,”. [18] Department of Drainage and Irrigation, Portal Banjir Terengganu [Online]. From http://jpsweb.terengganu.gov.my/ [19] National Security Council,Portal Bencana , [Online]. From http://portalbencana.mkn.gov [20] Wang, D., Ding, W., Yu, K., Wu, X., Chen, P., Small, D., L. & Islam, S., “Towards Longlead Forecasting of Extreme Flood Events: A Data Mining Framework for Precipitation Cluster Precursors Identification,”Proceedings of KDD 13, ACM,2013. [21] Bala, P.,K., A Technique for Mining Negative Association Rules, ACM ,January, 2009. [22] Lee, J. ,A.,Han., J & Chi,K.,H. “Mining Quantitative Association Rule of Earthquake Data,” ICHIT’09, August 27–29, 2009. [23] Danya, C., T. & Kumar, D., N., “Data mining for evolution of association rules for droughts and floods in India using climate inputs,” Journal Of Geophysical Research, Vol. 114, D02102, 2009 [24] Merceron, A. and Yacef, K., “Interestingness Measures for Association Rules in Educational Data,” 1st International Conference on Educational Data Mining (EDM08), Montreal, Canada , 2008. [25] IBM website. Big Data and Information Management. [Online]From : http://www01.ibm.com/software/data/bigdata/ [26] Department of Drainange and Irrigation Kelantan, eBanjir Portal. [Online] From http://did.kelantan.gov.my/. [27] World Research Online database .[Online] From www.scopus.com
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norman
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oai_dc
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13095 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=13095 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1423 90 90 762 2016-06-16 11:45:15 1423x762 7405-01-FH02-FIK-16-06057.jpg UniSZA Private Access A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules Journal of Theoretical and Applied Information Technology Over the last decade, flooding has been one of catastrophic disaster which causes economic damage, lost life and environmental deprivations. The need for flood prediction is rising since the decision maker lacks intelligent tools to predict flood areas. The data mining and geospatial visualization will test the temporal data which include river flow and rainfall data to find patterns and new information which then will use to predict areas that expose to flood. Since Terengganu situated at east peninsular, every year in October to March will be having heavy rainfall and increasing of sea levels. To this extent, we propose a framework to support flood prediction which the outcome will intention to better manage floods through prevention, protection and emergency response. 87 3 Asian Research Publishing Network Asian Research Publishing Network 512-519 [1] Paulo Cortez and Anibal Morais, “A Data Mining Approach to Predict Forest Fires using Meteorological Data”, 2007. [2] Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth, “Knowledge Discovery and Data Mining : Towards a Unifying Framework”, Proceedings of KDD-9,1996, pp.82-88. [3] Li Zheng,Chao Sheng, Liang Tang, Tao Li, Steve Luis and Shu-Ching Chen, “Applying Data Mining Techniques to Address Disaster Information Management Challenges on Mobile Devices,” Proceedings of KDD1,August 21-24, 2011, pp. 283-291. [4] Milan Cisty and Juraj Bezak, “The Application of Data Mining Methods for Short Time Flows Prediction in Flood Warning Systems,” Recent Advances in Continuum Mechanics, Hydrology and Ecology, 2013, pp. 92-97. [5] Ku Ruhana Ku,Mahamud, Norhayani Zakaria, Norliza Katuk, and Mohamad Shbier, “Flood Pattern Detection Using Sliding Window Technique,” Third Asia International Conference on Modelling & Simulation, 2009, pp. 45-50. [6] Giorgio Corani and Giorgio Guariso, “Coupling Fuzzy Modelling and Neural Networks for River Flood Prediction,” IEEE Transaction on Systems,Man,and Cybernetics-Part C:Applications and Reviews, Vol. 35, No 3, August 2005. [7] Fazlina A.R, Abd Manan S., Zainazlan M.Z. and Ramli Adnan, “Flood Water Level Modeling and Prediction Using NARX Neural Network: Case Study at Kelang River,” IEEE 10th International Colloqium on Signal Processing & its Application (CSPA2014), Mac 7-9, 2014, pp. 204-207. [8] Darka Mioc, Francois Anton and Brandford George Nickerson, “Decision Support for Flood Event Prediction and Monitoring,” 2007. [9] Yan Li and Manchun Li, “Application and Research on Flood Risk Assessment Decision Support System in the Lower Yellow River,” 2011. [10] Omar Al-Azzam, Deli Sarsar, Kirubel Seifu, Mehdi Mekni, “Flood prediction and Risk Assesment Using Advanced GeoVisualization and Data Mining Technique : A case study in the Red-Lake Valley,” Proceedings of Applied Computational Science, 2014. [11] Masond Bakhtyari Kia, Saled Piratesh, Biswajeet Pradhan, Ahmad Rodzi Mahmud, Wan Nor Azmin Sulaiman, Abbas Moradi, “An artificial neural network model for flood simulation using GIS : Johor River Basin, Malaysia,” Environ Earth Sci, Springer articles, December 31, 2011. [12] Daniela Stonajova, Pance Panov, Andrej Kobler, Saso Dzeroski and Katerina Taskova, “Learning to Predict Forest Fires With Different Data Mining Techniques,” 2006. [13] Ch. Lucas, St. Werder and H.-P. Bahr, “Information Mining for Disaster Management,” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (3/W49), 2007, pp. 75-80. [14] C.T.Dhanya and D.Nagesh Kumar.Fuzzy Association Rules for Prediction of Monsoon Rainfall,” 4th Indian International Conference on Artificial Intelligence (IICAI-09), 2009, pp. 1299-1309. [15] Thomas Landssdall-Welfare, Seatviga Sudjkar, et. Al, “On the Coverage of Science in the Media : A Big Data Study on the Impact of the Fukishima Disaster,” IEEE International Conference on Big Data, 2014, pp.60-66. [16] Qiang Yang.2006.10 Challenging Problems in Data Mining Research. [17] Mary McGlohon ,”Data Mining Disasters: a report,”. [18] Department of Drainage and Irrigation, Portal Banjir Terengganu [Online]. From http://jpsweb.terengganu.gov.my/ [19] National Security Council,Portal Bencana , [Online]. From http://portalbencana.mkn.gov [20] Wang, D., Ding, W., Yu, K., Wu, X., Chen, P., Small, D., L. & Islam, S., “Towards Longlead Forecasting of Extreme Flood Events: A Data Mining Framework for Precipitation Cluster Precursors Identification,”Proceedings of KDD 13, ACM,2013. [21] Bala, P.,K., A Technique for Mining Negative Association Rules, ACM ,January, 2009. [22] Lee, J. ,A.,Han., J & Chi,K.,H. “Mining Quantitative Association Rule of Earthquake Data,” ICHIT’09, August 27–29, 2009. [23] Danya, C., T. & Kumar, D., N., “Data mining for evolution of association rules for droughts and floods in India using climate inputs,” Journal Of Geophysical Research, Vol. 114, D02102, 2009 [24] Merceron, A. and Yacef, K., “Interestingness Measures for Association Rules in Educational Data,” 1st International Conference on Educational Data Mining (EDM08), Montreal, Canada , 2008. [25] IBM website. Big Data and Information Management. [Online]From : http://www01.ibm.com/software/data/bigdata/ [26] Department of Drainange and Irrigation Kelantan, eBanjir Portal. [Online] From http://did.kelantan.gov.my/. [27] World Research Online database .[Online] From www.scopus.com
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| spellingShingle |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| summary |
Over the last decade, flooding has been one of catastrophic disaster which causes economic damage, lost life and environmental deprivations. The need for flood prediction is rising since the decision maker lacks intelligent tools to predict flood areas. The data mining and geospatial visualization will test the temporal data which include river flow and rainfall data to find patterns and new information which then will use to predict areas that expose to flood. Since Terengganu situated at east peninsular, every year in October to March will be having heavy rainfall and increasing of sea levels. To this extent, we propose a framework to support flood prediction which the outcome will intention to better manage floods through prevention, protection and emergency response.
|
| title |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| title_full |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| title_fullStr |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| title_full_unstemmed |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| title_short |
A conceptual framework for predicting flood area in Terengganu during monsoon season using association rules
|
| title_sort |
conceptual framework for predicting flood area in terengganu during monsoon season using association rules
|