Classification model for water quality using machine learning techniques
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| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2015-09-03 10:47:07 |
| format | Restricted Document |
| id | 12268 |
| institution | UniSZA |
| internalnotes | Changjun, Z., & Zhenchun, H. (2009). Fuzzy neural network model and its application in water quality evaluation. Paper presented at the Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009, , 1 251-254. doi:10.1109/ESIAT.2009.45 Retrieved from www.scopus.com Chi, Z., Liang, G., Haibing, G., & Chuanyong, P. (2006). Pattern classification and prediction of water quality by neural network with particle swarm optimization. Paper presented at the Proceedings of the World Congress on Intelligent Control and Automation (WCICA), , 1 2864-2868. doi:10.1109/WCICA.2006.1712888 Retrieved from www.scopus.com Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data mining: A knowledge discovery approach. Data mining: A knowledge discovery approach (pp. 1-606) doi:10.1007/978-0-387-36795-8 Retrieved from www.scopus.com Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases, Retrieved from www.scopus.com Ismail, J. B. T. (2011). Assessment of kinta river water quality by using multivariate technique. Retrieved from www.scopus.com Makhtar, M., Neagu, D. C., & Ridley, M. J. (2011). Binary classification models comparison: On the similarity of datasets and confusion matrix for predictive toxicology applications doi:10.1007/978-3-642-23208-4_11 Retrieved from www.scopus.com Sen, P., & Xinhua, Z. (2009). A review of uncertainty methods employed in water quality modeling. Paper presented at the Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009, , 2 32-35. doi:10.1109/ESIAT.2009.227 Retrieved from www.scopus.com Sheppard, D., Tsegaye, T. D., Tadesse, W., Mckay, D., & Coleman, T. L. (2001). The application of remote sensing, geographic information systems, and global positioning system technology to improve water quality in northern alabama. Retrieved from www.scopus.com Wechmongkhonkon, S., Poomtong, N., & Areerachakul, S. (2012). Application of artificial neural network to classification surface water quality. World Academy of Science, Engineering and Technology, 6(9), 574-578. Retrieved from www.scopus.com Yunrong, X., & Jiang, L. (2009). Water quality prediction using LS-SVM with particle swarm optimization. Retrieved from www.scopus.com Zennaro, M., Floros, A., Dogan, G., Sun, T., Cao, Z., Huang, C., . . . Bagula, A. (2009). On the design of a water quality wireless sensor network (WQWSN): An application to water quality monitoring in malawi. Paper presented at the Proceedings of the International Conference on Parallel Processing Workshops, 330-336. doi:10.1109/ICPPW.2009.57 Retrieved from www.scopus.com |
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| spelling | 12268 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12268 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 784 1416 27 27 2015-09-03 10:47:07 1416x784 6568-01-FH02-FIK-15-03717.jpg UniSZA Private Access Classification model for water quality using machine learning techniques International Journal of Software Engineering and its Applications The problem of water pollution is increasing every day, due to the industries’ waste product disposal, migration of people from rural to urban areas, crowded population, untreated sewage disposal, wastewater and other harmful chemicals’ discharge from the industries. There is a need to resolve this problem for us to get good water that can be used for domestic purposes. This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. The paper analyzed and compared performance of various classification models and algorithms in order to identify the significant features that contributed in classifying water quality of Kinta River, Perak Malaysia. Five models with respective algorithms were tested and compared with their performance. In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. Generally, wastewater is harmful to our lives, and bringing scientific models in solving this problem is obligatory. 9 6 Science and Engineering Research Support Society Science and Engineering Research Support Society 45-52 Changjun, Z., & Zhenchun, H. (2009). Fuzzy neural network model and its application in water quality evaluation. Paper presented at the Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009, , 1 251-254. doi:10.1109/ESIAT.2009.45 Retrieved from www.scopus.com Chi, Z., Liang, G., Haibing, G., & Chuanyong, P. (2006). Pattern classification and prediction of water quality by neural network with particle swarm optimization. Paper presented at the Proceedings of the World Congress on Intelligent Control and Automation (WCICA), , 1 2864-2868. doi:10.1109/WCICA.2006.1712888 Retrieved from www.scopus.com Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data mining: A knowledge discovery approach. Data mining: A knowledge discovery approach (pp. 1-606) doi:10.1007/978-0-387-36795-8 Retrieved from www.scopus.com Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases, Retrieved from www.scopus.com Ismail, J. B. T. (2011). Assessment of kinta river water quality by using multivariate technique. Retrieved from www.scopus.com Makhtar, M., Neagu, D. C., & Ridley, M. J. (2011). Binary classification models comparison: On the similarity of datasets and confusion matrix for predictive toxicology applications doi:10.1007/978-3-642-23208-4_11 Retrieved from www.scopus.com Sen, P., & Xinhua, Z. (2009). A review of uncertainty methods employed in water quality modeling. Paper presented at the Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009, , 2 32-35. doi:10.1109/ESIAT.2009.227 Retrieved from www.scopus.com Sheppard, D., Tsegaye, T. D., Tadesse, W., Mckay, D., & Coleman, T. L. (2001). The application of remote sensing, geographic information systems, and global positioning system technology to improve water quality in northern alabama. Retrieved from www.scopus.com Wechmongkhonkon, S., Poomtong, N., & Areerachakul, S. (2012). Application of artificial neural network to classification surface water quality. World Academy of Science, Engineering and Technology, 6(9), 574-578. Retrieved from www.scopus.com Yunrong, X., & Jiang, L. (2009). Water quality prediction using LS-SVM with particle swarm optimization. Retrieved from www.scopus.com Zennaro, M., Floros, A., Dogan, G., Sun, T., Cao, Z., Huang, C., . . . Bagula, A. (2009). On the design of a water quality wireless sensor network (WQWSN): An application to water quality monitoring in malawi. Paper presented at the Proceedings of the International Conference on Parallel Processing Workshops, 330-336. doi:10.1109/ICPPW.2009.57 Retrieved from www.scopus.com |
| spellingShingle | Classification model for water quality using machine learning techniques |
| summary | The problem of water pollution is increasing every day, due to the industries’ waste product disposal, migration of people from rural to urban areas, crowded population, untreated sewage disposal, wastewater and other harmful chemicals’ discharge from the industries. There is a need to resolve this problem for us to get good water that can be used for domestic purposes. This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. The paper analyzed and compared performance of various classification models and algorithms in order to identify the significant features that contributed in classifying water quality of Kinta River, Perak Malaysia. Five models with respective algorithms were tested and compared with their performance. In assessing the result, the Lazy model using K Star algorithm was the best classification model among the five models had the most outstanding accuracy of 86.67%. Generally, wastewater is harmful to our lives, and bringing scientific models in solving this problem is obligatory. |
| title | Classification model for water quality using machine learning techniques |
| title_full | Classification model for water quality using machine learning techniques |
| title_fullStr | Classification model for water quality using machine learning techniques |
| title_full_unstemmed | Classification model for water quality using machine learning techniques |
| title_short | Classification model for water quality using machine learning techniques |
| title_sort | classification model for water quality using machine learning techniques |