Abstract
Due to pollution caused by the expansion of human activities and economic development, water quality has gradually deteriorated in many areas of the world. Therefore, analysis of water quality becomes one of the most essential issues of modern civilization. Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex phenomenon of water quality, have gained attention in recent years. With the ability to deal with both numeric and nominal information, and express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many fields. However, the application of RST has not been widely investigated in water quality analysis. The reducts generated by RST models become very time-consuming as the size of the problem increases. Using multinomial logistics regression (MLR) techniques to provide reducts of RST models, this investigation develops a hybrid Multinomial Logistic Regression and Rough Set Theory (MLRRST) model to analyze relations between degrees of water pollution and environmental factors in Taiwan. Empirical results indicate that the MLRRST model could analyze water qualities efficiently and accurately, and yield decision rules for the staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality.
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Abou-Elela SI, Nasr FA, Ibrahim HS, Badr NM, Askalany ARM (2008) Pollution prevention pays off in a board paper mill. J Clean Prod 16:330–334
Aguilera PA, Frensich AG, Torres JA, Castro H, Vidal JL, Canton M (2001) Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality. Water Res 35:4053–4062
Bello R, Nowe A, Caballero Y, Goméz Y, Vrancx P (2005) A model based on ant colony system and rough set theory to feature selection. In: Proceedings of the 2005 conference of genetic and evolutionary conference, 25–29, June 2005, Washington DC, USA, pp 275–276
Brtka V, Stotić E, Srdić B (2008) Automated extraction of decision rules for leptin dynamics—a rough sets approach. J Biomed Inform 41:667–674
Chatterjee S, Hadi AS, Price B (2000) Regression analysis by example, 3rd edn. Wiley, New York
Chen X, Li YS, Liu Z, Yin K, Li Z, Wai OWH, King B (2004) Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong. Cont Shelf Res 24:1827–1843
Chinatimes Foundation (2003) Taiwan River database. http://www.chinatimes.org.tw/features/river-new/rivers/river-bank.htm
Diamantopoulou MJ, Antonopoulos VZ, Papamichail DM (2007) Cascade correlation artificial neural networks for estimating missing monthly values of water quality parameters in rivers. Water Resour Manag 21:649–662
Dimitras AI, Slowinski R, Susmaga R, Zopounidis C (1999) Business failure prediction using rough sets. Eur J Oper Res 114(2):263–280
Djinovic JM, Popovic AR (2007) In situ influence of coal ash dump on the quality of neighboring surface and ground waters by applying correlation statistic analysis. Fuel 86:218–226
Driscoll CT, Driscoll KM, Mitchell MJ, Raynalc DJ (2003) Effects of acidic deposition on forest and aquatic ecosystems in New York State. Environ Pollut 123:327–336
Executive Yuan, Directorate-General of Budget, Accounting and Statistics, R.O.C. (2007) National statistics. http://61.60.106.82/pxweb/Dialog/statfile9.asp
Freitas AA, Lavington SH (1996) Speeding up knowledge discovery in large relational databases by means of a new discretization algorithm. In: Morrison R, Kennedy J (eds) Advances in databases: proceedings of the 14th British National Conference on Databases—BNCOD-14. LNCS, vol 1094. Springer, Berlin, pp 124–133
Hamlin C (2000) ‘Waters’ or ‘water’?—master narratives in water history and their implications for contemporary water policy. Water Policy 2:313–325
Hedar AR, Wang J, Fukushima M (2008) Tabu search for attribute reduction in rough set theory. Soft Comput 12(9):909–918
Hyun WY, Ditton RB (2006) Using multinomial logistic regression analysis to understand anglers willingness to substitute other fishing locations. In: Burns R, Robinson K (eds) Proceedings of the 2006 northeastern recreation research symposium, US Forest Service, Northern Research Station, Pennsylvania, USA, pp 248–255
Jensen R, Shen Q (2003) Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK workshop on computational intelligence, 1–3, September 2003, University of Bristol, UK, pp 15–22
Katko TS (1994) Water management in Finland. Eur Water Pollut Control 4(6):40–46
Kerber R (1992) ChiMerge: discretization of numeric attributes. In: Proceedings of the 10th international conference of artificial intelligence, 12–16, June 1992, AAAI Press/The MIT Press, Menlo Park, California, pp 123–128
Komarnisky LA, Christopherson RJ, Basu TK (2003) Sulfur: its clinical and toxicologic aspects. Nutr 19(1):54–61
Kralisch S, Fink M, Flügel WA, Beckstein C (2003) A neural network approach for the optimisation of watershed management. Environ Model Softw 18:815–823
Kuo JT, Wang YY, Lung WS (2006) A hybrid neural-genetic algorithm for reservoir water quality management. Water Res 40:1367–1376
Kurunç A, Yürekli K, Çevil O (2005) Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilirmak River, Turkey. Environ Model Softw 20:1195–1200
Leentvar J (1997) The need of the human factor in integrated water management. Eur Water Pollut Control 7(3):30–35
Liou SM, Lo SL, Hu CY (2003) Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Res 37:1406–1416
Maier HR, Morgan N, Chow CWK (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19:485–494
Manache G, Melching CS (2008) Identification of reliable regression- and correlation-based sensitivity measures for importance ranking of water-quality model parameters. Environ Model Softw 23:549–562
May RJ, Dandy GC, Maier HR, Nixon JB (2008) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ Model Softw 23:1289–1299
Mitchell MK, Stapp WB, Bixby K (2000) Field manual for water quality monitoring: an environmental education program for schools. Kendall/Hunt, Dubuque
Mushrif MM, Ray AK (2008) Color image segmentation: rough-set theoretic approach. Pattern Recognit Lett 29:483–493
Nakano T, Tayasu I, Yachi S, Yamada Y, Hosono T, Igeta A, Hyodo F, Ando A, Saitoh Y, Tanaka T, Wada E (2008) Effect of agriculture on water quality of Lake Biwa tributaries. Sci Total Environ 389:132–148
Nguyen SH, Skowron A (1995) Quantization of real value attributes: rough set and Boolean reasoning approaches. In: Wang PP (ed) Proceedings of the international workshop of rough sets and soft computing at second joint conference on information sciences. Elsevier, New York, pp 34–37
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer, Boston
Pawlak Z (2002) Rough sets and intelligent data analysis. Inf Sci 147:1–12
Qin XS, Huang GH (2009) An inexact chance-constrained quadratic programming model for stream water quality management. Water Resour Manag 23:661–695
Sainz A, Grande JA, de la Torre ML (2003) Odiel River, acid mine drainage and current characterisation by means of univariate analysis. Environ Int 29:51–59
Sanchis A, Segovia MJ, Gil JA, Heras A, Vilar JL (2007) Rough sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem). Eur J Oper Res 181(3):1554–1573
Shanmuganathan S, Sallis P, Buckeridge J (2006) Self-organising map methods in integrated modelling of environmental and economic systems. Environ Model Softw 21:1247–1256
Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22:464–475
Sikder IU, Gangopadhyay A (2007) Managing uncertainty in location services using rough set and evidence theory. Expert Syst Appl 32(2):386–396
Singh AP, Ghosh SK, Sharma P (2007) Water quality management of a stretch of river Yamuna. An interactive fuzzy multi-objective approach. Water Resour Manag 21:515–532
Skowron A, Rauser C (1992) The discernibility matrices and functions in information systems. In: Slowinski R (ed) Intelligent decision support. Handbook of applications and advances of the rough sets theory. Kluwer, Dordrecht, pp 331–362
Taiwan Environmental Law Library (2003) Water pollution control act. http://law.epa.gov.tw/en/laws/717336547.html
Tsumoto S (2000) Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic. Inf Sci 124(1–4):125–137
Wang X, Yang J, Jensen R, Liu X (2006) Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Comput Methods Programs Biomed 83:147–156
Wang X, Yang J, Teng X, Xia W, Jenson R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett 28:459–471
Water Resource Agency, Ministry of Economic Affairs (2006) The inquiry and supply system of water resources data. http://gweb.wra.gov.tw/wrweb/
Witlox F, Tindemans H (2004) The application of rough sets analysis in activity-based modeling. Opportunities and constraints. Expert Syst Appl 27(4):585–592
Yang HH, Liu TC, Lin YT (2007) Applying rough sets to prevent customer complaints for IC packaging foundry. Expert Syst Appl 32(1):151–156
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Pai, PF., Lee, FC. A Rough Set Based Model in Water Quality Analysis. Water Resour Manage 24, 2405–2418 (2010). https://doi.org/10.1007/s11269-009-9558-3
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DOI: https://doi.org/10.1007/s11269-009-9558-3