| _version_ |
1860797979380678656
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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 |
2024-08-30 11:26:36
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| format |
Restricted Document
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| id |
15240
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| institution |
UniSZA
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| internalnotes |
[1] Diane, S. 2004. Singapore’s Water Trade with Malaysia and Alternatives. Harvard University, Accessed at http://www.transboundarywaters.orst.edu/publications/abst_docs/related_ research/Segal-Singapore-Malaysia%2004.pdf, January 2015. [2] Moore, D. S. and McCabe, G. P. 1989. Introduction to the Practice of Statistics. W. H. Freeman, New York. [3] Altman, D. G. 1991. Practical Statistics for Medical Research. Chapman & Hall, London. 285–288. [4] Floyd, F. J. and Widaman, K. F. 1995. Factor Analysis in the Development and Refinement of Clinical Assessment Instruments. Psychological Assessment. 7(3): 286–299. [5] Gorsuch, R. L. 1990. Common Factor-Analysis Versus Component Analysis - Some Well and Little Known Facts. Multivariate Behavioral Research. 25(1): 33–39. [6] Meglen, R. R. 1992. Examining Large Databases: A Chemo-metric Approach Using Principal Component Analysis. Mar. Chem. (39): 217– 237. [7] Thompson, B., Daniel, L. G. 1996. Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines. Educational and Psychological Measurement. 56(2):197–208. [8] William, B., Brown, T. and Onsman, A. 2012. Exploratory Factor Analysis: A Five-step Guide for Novices. Australasian Journal of Paramedicine. 8(3): 1. [9] Yegnanarayana. 1994. Artificial Neural Networks for Pattern Recognition. Scidhanci. 19(2): 189–238. [10] Trubin, I.A. 2008. Exception Based Modelling and Forecasting. Proceedings of the Computer Measurement Group, Nevada. 7–12 December 2008. 353–364. [11] Juahir, H., Sharifuddin, M. Z., Mohd K. Y., Tengku, H. A. S., Mohd A., Mohd E. T., Mazlin, M. 2010. Spatial Water Quality Assessment of Langat River Basin (Malaysia) Using Environmetric Techniques. Environ Monit Assess. Doi: 10.1007/s10661-010-1411-x. [12] Juahir, H., Sharifuddin, M. Z., Ahmad, Z. A, Mohd, K. Y., Mazlin, M. 2009. Spatial Assessment of Langat River Water Quality Using Chemometrics. Journal of Environmental Monitoring. 12: 287–295. [13] Azid, A., Juahir, H., Latif, Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M. 2014. Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia. Water, Air & Soil Pollution. 225: 2063. [14] Goethals, P. L. M., Dedecker, A. P., Gabriels, W., Lek, S. and De Pauw, N. 2007. Applications of Artificial Neural Networks Predicting Macroinvertebrates in Freshwaters. Aquatic Ecology. 41(3): 491–508. [15] Rech, G. 2002. Forecasting with Artificial Neural Network Models. SSE/EFI Working Paper Series in Economics and Finance 491, Stockholm School of Economics [16] Azid, A., Juahir, H., Latif, M. T., Zain, S. M., Osman, M. R. 2013. Feedforward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. Journal of Environmental Protection. 4(12A). [17] Gelman, A. 2013. Two Simple Examples for Understanding Posterior PValues Whose Distributions are Far from Unform. Electronic Journal of Statistics. 7: 2595–2602. ISSN: 1935-7524. DOI: 10.1214/13-EJS854. [18] Saudi, A. S. M., Juahir, H., Azid, A., Yusof, K. M. K. K., Zainuddin, S. F. M., Osman, M. R. 2014. Spatial Assessment Of Water Quality Due To Land-Use Changes Along Kuantan River Basin. From Sources to Solution. 2014: 297–300.
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15240 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15240 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 7 Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 1.7 Adobe Acrobat Pro DC 20.6.20042 2024-08-30 11:26:36 6033-01-FH02-ESERI-15-03318.pdf UniSZA Private Access Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin Jurnal Teknologi Flood is a major problem in Johor river basin, which normally happened during monsoon season. However, in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class. 74 1 165-170 [1] Diane, S. 2004. Singapore’s Water Trade with Malaysia and Alternatives. Harvard University, Accessed at http://www.transboundarywaters.orst.edu/publications/abst_docs/related_ research/Segal-Singapore-Malaysia%2004.pdf, January 2015. [2] Moore, D. S. and McCabe, G. P. 1989. Introduction to the Practice of Statistics. W. H. Freeman, New York. [3] Altman, D. G. 1991. Practical Statistics for Medical Research. Chapman & Hall, London. 285–288. [4] Floyd, F. J. and Widaman, K. F. 1995. Factor Analysis in the Development and Refinement of Clinical Assessment Instruments. Psychological Assessment. 7(3): 286–299. [5] Gorsuch, R. L. 1990. Common Factor-Analysis Versus Component Analysis - Some Well and Little Known Facts. Multivariate Behavioral Research. 25(1): 33–39. [6] Meglen, R. R. 1992. Examining Large Databases: A Chemo-metric Approach Using Principal Component Analysis. Mar. Chem. (39): 217– 237. [7] Thompson, B., Daniel, L. G. 1996. Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines. Educational and Psychological Measurement. 56(2):197–208. [8] William, B., Brown, T. and Onsman, A. 2012. Exploratory Factor Analysis: A Five-step Guide for Novices. Australasian Journal of Paramedicine. 8(3): 1. [9] Yegnanarayana. 1994. Artificial Neural Networks for Pattern Recognition. Scidhanci. 19(2): 189–238. [10] Trubin, I.A. 2008. Exception Based Modelling and Forecasting. Proceedings of the Computer Measurement Group, Nevada. 7–12 December 2008. 353–364. [11] Juahir, H., Sharifuddin, M. Z., Mohd K. Y., Tengku, H. A. S., Mohd A., Mohd E. T., Mazlin, M. 2010. Spatial Water Quality Assessment of Langat River Basin (Malaysia) Using Environmetric Techniques. Environ Monit Assess. Doi: 10.1007/s10661-010-1411-x. [12] Juahir, H., Sharifuddin, M. Z., Ahmad, Z. A, Mohd, K. Y., Mazlin, M. 2009. Spatial Assessment of Langat River Water Quality Using Chemometrics. Journal of Environmental Monitoring. 12: 287–295. [13] Azid, A., Juahir, H., Latif, Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M. 2014. Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia. Water, Air & Soil Pollution. 225: 2063. [14] Goethals, P. L. M., Dedecker, A. P., Gabriels, W., Lek, S. and De Pauw, N. 2007. Applications of Artificial Neural Networks Predicting Macroinvertebrates in Freshwaters. Aquatic Ecology. 41(3): 491–508. [15] Rech, G. 2002. Forecasting with Artificial Neural Network Models. SSE/EFI Working Paper Series in Economics and Finance 491, Stockholm School of Economics [16] Azid, A., Juahir, H., Latif, M. T., Zain, S. M., Osman, M. R. 2013. Feedforward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. Journal of Environmental Protection. 4(12A). [17] Gelman, A. 2013. Two Simple Examples for Understanding Posterior PValues Whose Distributions are Far from Unform. Electronic Journal of Statistics. 7: 2595–2602. ISSN: 1935-7524. DOI: 10.1214/13-EJS854. [18] Saudi, A. S. M., Juahir, H., Azid, A., Yusof, K. M. K. K., Zainuddin, S. F. M., Osman, M. R. 2014. Spatial Assessment Of Water Quality Due To Land-Use Changes Along Kuantan River Basin. From Sources to Solution. 2014: 297–300.
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| spellingShingle |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
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| summary |
Flood is a major problem in Johor river basin, which normally happened during monsoon season. However, in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.
|
| title |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
|
| title_full |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
|
| title_fullStr |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
|
| title_full_unstemmed |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
|
| title_short |
Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin
|
| title_sort |
flood risk pattern recognition using integrated chemometric method and artificial neural network: a case study in the johor river basin
|