Determination of the principal factors of river water quality through cluster analysis method and its prediction
Liang GUO, Ying ZHAO, Peng WANG
Determination of the principal factors of river water quality through cluster analysis method and its prediction
In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (CODMn) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the CODMn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.
water quality forecast / principal factor / cluster analysis method / artificial neural network
[1] |
Gallant S I. Neural Network Learning and Expert Systems. Massachusetts: MIT Press, 1993
|
[2] |
Smith M. Neural Networks for Statistical Modelling. New York: van Nostrand Reinhold, 1994
|
[3] |
Singh K P, Basant A, Malik A, Jain G. Artificial neural network modeling of the river water quality—a case study. Ecological Modelling, 2009, 220(6): 888–895
CrossRef
Google scholar
|
[4] |
Maier H R, Jain A, Dandy G C, Sudheer K PKPS. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software, 2010, 25(8): 891–909
CrossRef
Google scholar
|
[5] |
Gardner M W, Dorling S R. Artificial neural network (the multilayer perceptron) —a review of applications in the atmospheric sciences. Atmospheric Environment, 1998, 32(14–15): 2627–2636
CrossRef
Google scholar
|
[6] |
Rogers L L, Dowla F U. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resources Research, 1994, 30(2): 457–481
CrossRef
Google scholar
|
[7] |
Raman H, Chandramouli V. Deriving a general operating policy for reservoirs using neural networks. Journal of Water Resources Planning and Management, 1996, 122(5): 342–347
CrossRef
Google scholar
|
[8] |
Wen C W, Lee C S. A neural network approach to multiobjective optimization for water quality management in a river basin. Water Resources Research, 1998, 34(3): 427–436
CrossRef
Google scholar
|
[9] |
Lek S, Guegan J F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 1999, 120(2–3): 65–73
CrossRef
Google scholar
|
[10] |
Kuo Y M, Liu C W, Lin K H. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Research, 2004, 38(1): 148–158
CrossRef
Pubmed
Google scholar
|
[11] |
Dogan E, Sengorur B, Koklu R. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management, 2009, 90(2): 1229–1235
CrossRef
Pubmed
Google scholar
|
[12] |
Dixon M, Gallop J, Lambert S, Lardon L, Healy J, Steyer J. Data mining to support anaerobic WWTP monitoring. Control Engineering Practice, 2007, 15(8): 987–999
CrossRef
Google scholar
|
[13] |
Yin Y F. A proximate dynamics model for data mining. Expert Systems with Applications, 2009, 36(6): 9819–9833
CrossRef
Google scholar
|
[14] |
Liao X Y. The application research on spatial data mining in surface water quality evaluation and prediction. Dissertation for the Master Degree. Changchun: Northeast Normal University, 2006 (in Chinese)
|
[15] |
Holger R M, Nicolas M, Maier H, Christopher W K C. Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environmental Modelling & Software, 2004, 19(5): 485–494
CrossRef
Google scholar
|
[16] |
Grishma R S, Heidar M, Shankararaman C. Predicting contaminant removal during municipal drinking water nanofiltration using artificial neural networks. Journal of Membrane Science, 2003, 212(1–2): 99–112
|
[17] |
Nikolaos M, Mastrogiannis N, Boutsinas B, Giannikos I, Basilis B, Ioannis G. A method for improving the accuracy of data mining classification algorithms. Computers & Operations Research, 2009, 36(10): 2829–2839
CrossRef
Google scholar
|
[18] |
Chen Q W, Chen Q, Mynett A E,Arthur E M. Integration of data mining techniques and heuristic knowledge in fuzzy logic modeling of eutrophication in Taihu Lake. Ecological Modelling, 2003, 162(1–2): 55–67
CrossRef
Google scholar
|
[19] |
Yang Y B, Lin H, Guo Z Y, Jiang J X. A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis. Computers & Geosciences, 2007, 33(1): 20–30
CrossRef
Google scholar
|
[20] |
Chu B H, Tsai M S, Ho C S. Toward a hybrid data mining model for customer retention. Knowledge-Based Systems, 2007, 20(8): 703–718
CrossRef
Google scholar
|
[21] |
Massart D L, Kaufman L. The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis. New York: Wiley, 1983
|
[22] |
Willet. Similarity and Clustering in Chemical Information Systems, Research Studies Press. New York: Wiley, 1987
|
[23] |
Razmkhah H, Abrishamchi A, Torkian A. Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: a case study on Jajrood River (Tehran, Iran). Journal of Environmental Management, 2010, 91(4): 852–860
CrossRef
Pubmed
Google scholar
|
[24] |
Chang Q L, Zhou H Q, Hou C J. Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material. Journal of China University of Mining and Technology, 2008, 18(4): 551–555
CrossRef
Google scholar
|
[25] |
Feng L H, Lu J. The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications, 2010, 37(4): 2974–2977
CrossRef
Google scholar
|
[26] |
Palani S, Liong S Y, Tkalich P. An ANN application for water quality forecasting. Marine Pollution Bulletin, 2008, 56(9): 1586–1597
CrossRef
Pubmed
Google scholar
|
[27] |
Grivas G, Chaloulakou A. Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 2006, 40(7): 1216–1229
CrossRef
Google scholar
|
[28] |
Lee T L, Jeng D S. Application of artificial neural networks in tide-forecasting. Ocean Engineering, 2002, 29(9): 1003–1022
CrossRef
Google scholar
|
[29] |
Imrie C E, Durucan S, Korre A. River flow prediction using artificial neural networks: generalisation beyond the calibration range. Journal of Hydrology (Amsterdam), 2000, 233(1–4): 138–153
CrossRef
Google scholar
|
[30] |
Kim M, Choi C Y, Gerba C P. Source tracking of microbial intrusion in water systems using artificial neural networks. Water Research, 2008, 42(4–5): 1308–1314
CrossRef
Pubmed
Google scholar
|
[31] |
Jeng D S, Daeho C, Michael B. Neural network model for the prediction of wave-induced liquefaction potential. Ocean Engineering, 2004, 31(17–18): 2073–2086
CrossRef
Google scholar
|
[32] |
Chong E K P, Zak S H. An Introduction to Optimization. New York: Wiley, 1996
|
[33] |
Moller M F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4): 525–533
CrossRef
Google scholar
|
[34] |
Adamowski J, Sun K. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology (Amsterdam), 2010, 390(1–2): 85–91
|
[35] |
Tsai M J, Li C H, Chen C C. Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. Journal of Materials Processing Technology, 2008, 208(1–3): 270–283
|
/
〈 | 〉 |