Author : Aswin Kumar P M 1
Date of Publication :11th June 2019
Abstract: Data mining is the exploration and scrutiny of large quantity of information that is able to discover meaningful and significant patterns. This paper studies various data mining techniques for prediction of water quality. This paper reviews the models and various evaluation methods that describe and distinguish the classes of water quality. Various data mining techniques like Artificial neural networks, Naïve bayes, Back propogation algorithm, KNN etc has been explored in this paper.
Reference :
-
- S. Palani, S. Liong, P. Tkalich, “An ANN application for water quality forecasting”, Marine Pollution Bulletin 56 (2008) 1586–1597.
- W. Chine, T. Wang, L. Chen, C. Kou, “Artificial Neural Networks for Water Quality Prediction in a Reservoir”, 2009 Second International Workshop on Computer Science and Engineering.
- C. Zhu, Z. Hao, “Fuzzy Neural Network Model and its Application in Water Quality Evaluation”, 2009 International Conference on Environmental Science and Information Application Technology.
- J. Lu, T. Huang, “Data Mining on Forecast Raw Water Quality from Online Monitoring Station Based on Decision-making Tree”, 2009 Fifth International Joint Conference on INC, IMS and IDC.
- M. Qun, G. Ying, L. Zhiqiang, T. Xiaohui, “Application of Comprehensive Water Quality Identification Index in Water Quality Assessment of River”, Global Congress on Intelligent Systems.
- L. Chaunqi, W. Wei, “Assessment of the water quality near the dam area of Three Gorges Reservoir based on Bayes”, The 1st International Conference on Information Science and Engineering (ICISE2009).
- Z. Xing, Q. Fu, D. Liu, “Water Quality Evaluation by the Fuzzy Comprehensive Evaluation based on EW Method”, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
- Y. Khan, C. S. See, “Predicting and Analyzing Water Quality using Machine Learning: A Comprehensive Model”.
- C. Photphanloet, W.Treeratanajaru, N. Cooharojananone, R. Lipikorn, “Biochemical Oxygen Demand Prediction for Chaophraya River Using Alpha-Trimmed ARIMA Model” 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).
- X. Yunrong and L. Jiang, “Water quality prediction using LS-SVM with particle swarm optimization”, Second international workshop on knowledge discovery and Data mining, IEEE, (2009)
- H. Bokar, J. Tang, and N. F. Lin, “Groundwater quality and contamination index mapping in Changchun city, China”, Chinese Geographical Science, Vol. 14, No. 1, pp. 63–70, 2004.
- S. Wechmongkhonkon, N.Poomtong, S. Areerachakul, “Application of Artificial Neural Network to Classification Surface Water Quality
- Y. Park, K. H. Cho, J. Park, S. M. Cha, and J. H. Kim, “Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.,” Sci. Total Environ., vol. 502, pp. 31–41, Jan. 2015.
- S. Maiti and R. K. Tiwari, “A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction,” Environ. Earth Sci., vol. 71, no.7, pp. 3147–3160, 2013.
- M.J. Diamantopoulou, V.Z. Antonopoulos and D.M. Papamichail “The Use of a Neural Network Technique for the Prediction of Water Quality Parameters of Axios River in Northern Greece”, Journal 0f Operational Research, Springer-Verlag, Jan 2005, pp. 115-125.