Abstract
Rivers are playing an important role in human life and wildlife but due to pollution the quality of river water is extremely deteriorated. The assessment of water quality is a very indeterminate task and associates a lot of uncertainty and subjectivity in the decision making. To cope with this situation, computational intelligence techniques are found competent to develop models for water quality assessment. One of the computational intelligence techniques, fuzzy logic is used to implement such models. In this manuscript, a fuzzy knowledge-based system is developed to classify the water quality of river Ganga in three groups. The open-access software ‘Guaje’ is used to implement the proposed model. The analysis of the results is presented in terms of interpretability and accuracy which are found satisfactory.
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References
F.B. Semiromi, A.H. Hassani, A. Torabian, A.R. Karbassi, H. Lotfi, Water quality index development using fuzzy logic: a case study of the Karoon River of Iran. Afr. J. Biotechnol. 1(50), 10125–10133 (2011)
A. Lermontov, L. Yokoyama, M. Lermontov, M.A.S. Machado, River quality analysis using fuzzy water quality index: Ribeirado Iguape river water shed. Braz. Ecol. Indic. 9(6), 1188–1197 (2009)
Y. Lcaga, Fuzzy evaluation of water quality classification. Ecol. Ind. 7(3), 710–718 (2007)
R. Li, Z. Zou, A. An, Water quality assessment an in on river based on fuzzy water pollution index method. J. Environ. Sci. 50, 87–92 (2016)
H. Gharibi, A.H. Mahvi, R. Nabizadeh, H. Arabalibeik, M. Yunesian, M.H. Sowlat, A novel approach in water quality assessment based on fuzzy logic. J. Environ. Manag. 112, 87–95 (2012)
W. Ocampo-Duque, N. Ferre-Huguet, J.L. Domingo, M. Schuhmacher, Assessing water quality in rivers with fuzzy inference systems: a case study. Environ. Int. 32(6), 733–742 (2006)
S. Dahiya, B. Singh, S. Gaur, V.K. Garg, H.S. Kushwaha, Analysis of ground water quality using fuzzy synthetic evaluation. J. Hazard. Mater. 147(3), 938–946 (2007)
N.-B. Chang, H.W. Chen, S.K. Ning, Identification of river water quality using the fuzzy synthetic evaluation approach. J. Environ. Manage. 63(3), 293–305 (2001)
F. Nasiri, I. Maqsood, G. Huang, N. Fuller, Water quality index: a fuzzy river pollution decision support expert system. J. Water Resour. Plan. Manag. 133(2) (2007)
S.K. Kumar, R. Bharani, N.S. Magesh, P.S. Godson, N. Chnadrasekar, Hydrogeochemistry and ground water quality appraisal of part of south Chennai wastal aquifers, Tamilnadu, India using WQI and fuzzy logic method. Appl. Water Sci. 4(4), 341–350 (2014)
V.R. Raman, R. Bouwmeester, S. Mohan, Fuzzy logic water quality index and importance of water quality parameters, Air. Soil Water Res. 2, 51–59 (2009)
H. Gharibi, M.H. Sowlat, A.H. Mahvi, H. Mahmoudzadeh, H. Arabalibeik, M. Keshararz, N. Karimzadeh, G. Hassani, Development of dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems. Ecol. Ind. 20, 228–237 (2012)
P. Abdullah, S. Waseem, B.V. Raman, I.-U. Mohsin, Development of a new water quality model using fuzzy logic system for Malaysia. Open Environ. Sci. 2, 101–106 (2008)
A. Mourhir, T. Rachidi, M. Karim, River water quality index for Morocco using a fuzzy inference system. Environ. Syst. Res. 3(21) (2014)
J.J. Carbajal-Hernandez, L.P. Sánchez-Fernandez, J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad, Immediate water quality assessment in shrimp culture using fuzzy inference system. Expert Syst. Appl. 39(12), 10571–10582 (2012)
D. Wang, V.P. Singh, Y. Zhu, Hybrid fuzzy and optimal modeling for water quality evaluation. Water Resour. Res. 43, 1–10 (2007)
S.R.M.M. Roveda, A.P.M. Bondanca, J.G.-S. Silva, J.A.F. Roveda, A.H. Rosa, Development of a water quality index using a fuzzy logic: a case study for the Sorocaba river, in International Conference on Fuzzy Systems, Barcelona, Spain (2010), pp. 18–23
S.S. Mahapatra, S.K. Nanda, B.K. Panigrahy, A cascaded fuzzy inference system for Indian river water quality prediction. Adv. Eng. Softw. 42(10), 787–796 (2011)
A.M. Jinturkar, S.S. Deshmukh, S.V. Agarkar, G.R. Chavhan, Determination of water quality index by fuzzy logic approach: a case of ground water in an Indian Town. World Sci. Technol. 61(8), 1987–1994 (2010)
D. Scannapieco, V. Naddeo, T. Zarra, V. Belgiorno, River water quality assessment: a comparison of binary and fuzzy logic based approaches. Ecol. Eng. 47, 132–140 (2012)
P.K. Shukla, S.P. Tripathi, A review on the interpretability-accuracy trade-off in evolutionary multi-objective fuzzy systems (EMOFS). Inf. Sci. 3(3), 256–277
P.K. Shukla, S.P. Tripathi, A survey on interpretability-accuracy trade-off in evolutionary fuzzy systems, in Fifth International Conference on Genetic and Evolutionary Computing (ICGEC), 29 August–01 September (2011), pp. 97–101
P.K. Shukla, S.P. Tripathi, Interpretability issues in evolutionary multi-objective fuzzy knowledge base system, in Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), ABV-IIITM, Gwalior (2012), pp. 473–484
P.K. Shukla, S.P. Tripathi, Handling high dimensionality and interpretability-accuracy trade-off issues in evolutionary multi-objective fuzzy classifiers. Int. J. Sci. Eng. Res. 5(6), 665–670 (2014)
S.P. Tripathi, P.K. Shukla, Uncertainty handling using fuzzy logic in rule based systems. Int. J. Adv. Sci. Technol. 45, 31–46 (2012)
P.K. Shukla, S.P. Tripathi, On the design of interpretable evolutionary fuzzy systems (I-EFS) with improved accuracy, in International Conference on Communication Systems, Sept. 14– Sept. 15, Phagwara, India (2012), pp. 11–14
P.K. Shukla, S.P. Tripathi, Interpretability and accuracy issues in evolutionary multi-objective fuzzy classifiers. Int. J. Soft Comput. Netw. 1(1), 55–69 (2016)
P. Chandra, D. Agarawal, P.K. Shukla, MOBI-CLASS: a fuzzy knowledge based system for mobile handset classification, in 7th International Conference on Soft Computing for Problem Solving (SocProS), IIT Bhubaneswar, India, Dec. 23–24 (2017)
https://nmcg.nic.in/NamamiGanga.aspx. Accessed 01 March 2019
J.M. Alonso, L. Magdalena, HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft. Comput. 15(10), 1959–1980 (2011)
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Shukla, P.K. (2020). Development of Fuzzy Knowledge-Based System for Water Quality Assessment in River Ganga. In: Nagar, A., Deep, K., Bansal, J., Das, K. (eds) Soft Computing for Problem Solving 2019 . Advances in Intelligent Systems and Computing, vol 1139. Springer, Singapore. https://doi.org/10.1007/978-981-15-3287-0_2
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DOI: https://doi.org/10.1007/978-981-15-3287-0_2
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