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Introduction

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Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model

Part of the book series: SpringerBriefs in Water Science and Technology ((BRIEFSWATER))

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Abstract

This chapter discuss the introduction on the subject matter including some related literature reviews as well as the motivation of the study. The background of the study is presented in details.

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References

  1. Abdullah L, Zakaria N (2012) Matrix driven multivariate fuzzy linear regression model in car sales. J Appl Sci (Faisalabad) 12(1):56–63

    Google Scholar 

  2. Ahmad F, Yahaya S (2017) First-order interaction multiple regressions model on water quality index in Manjung River and its tributaries

    Google Scholar 

  3. Asadollahfardi G (2015) Water quality management: assessment and interpretation. Springer, Berlin

    Google Scholar 

  4. Asai HTSUK, Tanaka S, Uegima K (1982) Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybern 12:903–907

    Google Scholar 

  5. Bakar AAA, Pauzi AM, Mohamed AA, Sharifuddin SS, Idris FM (2018) Preliminary analysis on the water quality index (WQI) of irradiated basic filter elements. In: IOP conference series: materials science and engineering, vol 298, no 1. IOP Publishing, p 012005

    Google Scholar 

  6. Bhavyashree S, Mishra M, Girisha GC (2017) Fuzzy regression and multiple linear regression models for predicting mulberry leaf yield: a comparative study. Int J Agric Stat Sci 13(1):149–152

    Google Scholar 

  7. Boyd CE (2020) Water quality: an introduction, 2nd edn. Springer, Berlin

    Google Scholar 

  8. Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index—do we dare

    Google Scholar 

  9. Canter LW (2018) Environmental impact of water resource projects. CRC Press

    Google Scholar 

  10. Chang PT, Lee ES (1996) A generalized fuzzy weighted least-squares regression. Fuzzy Sets Syst 82(3):289–298

    Google Scholar 

  11. Chen YS (2001) Outliers detection and confidence interval modification in fuzzy regression. Fuzzy Sets Syst 119(2):259–272

    Google Scholar 

  12. Choi SH, Buckley JJ (2008) Fuzzy regression using least absolute deviation estimators. Soft Comput 12(3):257–263

    Google Scholar 

  13. Cude CG (2001) Oregon water quality index a tool for evaluating water quality management effectiveness 1. JAWRA J Am Water Resour Assoc 37(1):125–137

    Google Scholar 

  14. Dinius SH (1987) Design of an index of water quality 1. JAWRA J Am Water Resour Assoc 23(5):833–843

    Google Scholar 

  15. D’Urso P, Gastaldi T (2000) A least-squares approach to fuzzy linear regression analysis. Comput Stat Data Anal 34(4):427–440

    Google Scholar 

  16. D’Urso P, Gastaldi T (2001) Linear fuzzy regression analysis with asymmetric spreads. In: Advances in classification and data analysis. Springer, Berlin, Heidelberg, pp 257–264

    Google Scholar 

  17. Dojlido J, Raniszewski J, Woyciechowska J (1994) Water quality index applied to rivers in the Vistula river basin in Poland. Environ Monit Assess 33(1):33–42

    Google Scholar 

  18. Duca G (2014) Management of water quality in Moldova. Springer International Publishing, Cham

    Google Scholar 

  19. Hidayah Mohamed Isa N, Othman M, Karim SAA (2018) Multivariate matrix for fuzzy linear regression model to analyse the taxation in Malaysia. Int J Eng Technol 7(4.33):78–82. http://dx.doi.org/10.14419/ijet.v7i4.33.23490

  20. Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306

    Google Scholar 

  21. Krätschmer V (2006) Least-squares estimation in linear regression models with vague concepts. Fuzzy Sets Syst 157(19):2579–2592

    Google Scholar 

  22. Kung HT, Ying LG, Liu YC (1992) A complementary tool to water quality index: fuzzy clustering analysis 1. JAWRA J Am Water Resour Assoc 28(3):525–533

    Google Scholar 

  23. Landwehr, J. M., & Deininger, R. A. (1976). A comparison of several water quality indexes. Journal (Water Pollution Control Federation), 954–958

    Google Scholar 

  24. Li Y, Nzudie HLF, Zhao X, Wang H (2020) Addressing the uneven distribution of water quantity and quality endowment: physical and virtual water transfer within China. In: SpringerBriefs in water science and technology. Springer, Singapore

    Google Scholar 

  25. Marcello B, George T (2013) Water quality modelling for rivers and streams. Springer, Water Science and Technology Library

    Google Scholar 

  26. Mohammadpour R, Shaharuddin S, Chang CK, Zakaria NA, Ab Ghani A, Chan NW (2015) Prediction of water quality index in constructed wetlands using support vector machine. Environ Sci Pollut Res 22(8):6208–6219

    Google Scholar 

  27. Ott W (1978) Water quality indices: a survey of indices used in the United States, vol 1. Environmental Protection Agency, Office of Research and Development, Office of Monitoring and Technical Support

    Google Scholar 

  28. Pan NF, Lin TC, Pan NH (2009) Estimating bridge performance based on a matrix-driven fuzzy linear regression model. Autom Constr 18(5):578–586

    Google Scholar 

  29. Sakawa M, Yano H (1992) Multiobjective fuzzy linear regression analysis for fuzzy input-output data. Fuzzy Sets Syst 47(2):173–181

    Google Scholar 

  30. Sii HI, Sherrard JH, Wilson TE (1993) A water quality index based on fuzzy set theory. In: Environmental engineering-conference. American Society of Civil Engineers, pp 1727–1727

    Google Scholar 

  31. Sousa SIV, Martins FG, Pereira MC, Alvim-Ferraz MCM, Ribeiro H, Oliveira M, Abreu I (2010) Use of multiple linear regressions to evaluate the influence of O3 and PM10 on biological pollutants. Int J Environ Sci Eng 2(2):107–112

    Google Scholar 

  32. Tsuzuki Y (2014) Pollutant discharge and water quality in urbanisation. In: SpringerBriefs in water science and technology. Springer International Publishing, Cham

    Google Scholar 

  33. Water Pollution (2011) Retrieved from http://malaysianh2o.blogspot.com/2011/04/water-pollution.html

  34. Wu HC (2003) Fuzzy estimates of regression parameters in linear regression models for imprecise input and output data. Comput Stat Data Anal 42(1–2):203–217

    Google Scholar 

  35. Xu R, Li C (2001) Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets Syst 119(2):215–223

    Google Scholar 

  36. Yen J, Langari R (1999) Fuzzy logic: intelligence, control, and information, vol 1. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  37. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Google Scholar 

  38. Zainordin NS, Ramli NA, Elbayoumi M (2017) Distribution and temporal behaviour of O3 and NO2 near selected schools in Seberang Perai, Pulau Pinang and Parit Buntar, Perak, Malaysia. Sains Malays 46(2):197–207

    Google Scholar 

  39. Zali MA, Retnam A, Juahir H, Zain SM, Kasim MF, Abdullah B, Saadudin SB (2011) Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia. World Appl Sci J 14:60–65

    Google Scholar 

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Author information

Authors and Affiliations

  1. Fundamental and Applied Sciences Department and Centre for Smart Grid Energy Research (CSMER), Institute of Autonomous System, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Dr. Samsul Ariffin Abdul Karim

  2. Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Nur Fatonah Kamsani

Authors
  1. Dr. Samsul Ariffin Abdul Karim
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  2. Nur Fatonah Kamsani
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Corresponding author

Correspondence to Samsul Ariffin Abdul Karim .

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Karim, S., Kamsani, N. (2020). Introduction. In: Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. SpringerBriefs in Water Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-3485-0_1

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