Mamdani-type fuzzy inference system for irrigational water quality

Authors

  • S. Ponsadai Lakshmi Department of Science & Humanities, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India
  • C. Gopi Department of Science & Humanities, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India
  • P. Adwin Jose Department of Science & Humanities, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India

Keywords:

Mamdani fuzzy inference system, USSL diagram, Irrigational water quality groundwater resources, Mayiladuthurai

Abstract

This paper aims to evaluate the quality of groundwater resources used for irrigation purposes using a Mamdani-type fuzzy inference system (MFIS). The MFIS is used to resolve ambiguities and uncertainties in economic, social, and natural systems and also facilitates the capture of expert knowledge in ways similar to human reasoning and thought processes. In this study, 20 groundwater samples were collected from various locations within the Mayiladuthurai district, Tamil Nadu, India, between January 2016 and December 2019. These samples underwent physical and chemical analyses to assess the suitability of the collected water resources for irrigation. The analysis utilizes the Mamdani Fuzzy Inference System, which combines values of Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR). Additionally, methods from the US Salinity Laboratory Staff were also employed. Ultimately, the groundwater quality in Mayiladuthurai is graded for irrigation use by this method. The results indicate that the MFIS reduces imprecision and uncertainty in data handling through the fuzzy membership function. The comparison of irrigation suitability results clearly demonstrates that the proposed MFIS method offers an improved assessment of the irrigation water quality level of the studied groundwater resources.

Dimensions

Hadeel A Al Saleh, Samir Saify & Nariman Y Othuman, “Spatial distribution of ground water quality parameters in Al-Najaf city using GIS and Geostatistics Techniques,” IOP onf. Series: Earth and Environmental science 952 (2022) 012003. http://dx.doi.org/10.1088/1755-1315/952/1/012003.

Mufeed Batarseh, Emad Imreizeeq, Seyda Tilev, Mohammad Al Alaween, Wael Suleiman, Abdulla Mohammed Al Remeithi, Mansoor Khamees Al Tamimi, & Majdy Al Alawneh, “Assessment of groundwater quality for irrigation in the arid regions using irrigation water quality index (IWQI) and GIS-Zoning maps: Case study from Abu Dhabi Emirate, UAE,” Water quality for agriculture 14 (2021) 100611. https://doi.org/10.1016/j.gsd.2021.100611.

. C.Simsek and O. Gunduz, “IWQ Index: A GIS-integrated technique to assess the irrigation water quality,” Environ. Monit. Assess. 128 (2007) 277. https://doi.org/10.1007/s10661-006-9312-8.

F. M. Eaton, “Significance of carbonate in irrigation water, Soil Science, fuzzy-based air quality index (FAQI) for air quality assessment,” Atmospheric Environment 39 (1950) 123. https://doi.org/10.1097/00010694-195002000-00004.

Mohammad Zaman, Shabbir A Shahid and Lee Heng, “Guideline for salinity assessment, Mitigation and Adaptation using nuclear and related techniques,” Springer, Cham, 105 (2018) 113. http://dx.doi.org/10.1007/978-3-319-96190-3.

C. A Quist-Jensen, F. Macedonio and E. Drioli, “Membrane technology for water production in agriculture: Desalination and waste water reuse,” Desalination 364 (2015) 17. http://dx.doi.org/10.1016/j.desal.2015.03.001.

R.S. Ayers, “Quality of water for irrigation,” Journal of Irrigation and Drainage Div. ASCE 103, (1977) 135. https://doi.org/10.1023/A:1005834807873.

W. Silvert, “Fuzzy indices of environmental conditions” Ecological Modelling 130 (2000) 111. https://doi.org/10.1016/S0304-3800(00)00204-0

W. Ocampo-Duque, N. Ferre-Huquet, J. L. Domingo, and M. Schuhmacher, “Assessing water quality in rivers with fuzzy inference systems: a case study,” Environment International 32 (2006) 733. https://doi.org/10.1016/j.envint.2006.03.009.

S. Lio and S. L. Lo, “A fuzzy index model for trophic status evaluation of reservoir waters,” Water Research 96 (2004) 35. https://doi.org/10.1016/j.watres.2005.01.014.

S. Dahiya, D. Datta and H. S. Kushwaha, “A fuzzy synthetic evaluation approach for assessment of physicochemical quality of groundwater for drinking purposes,” Environmental Geology 8 (2005) 158. doi:10.1016/j.jhazmat.2007.01.119.

W. O. Duque, N. F. Huguet, J. L. Domingo and M.Schuhmacher, “Assessing water quality in rivers with fuzzy inference systems: a case study,” Environment International 32 (2006) 733. https://doi.org/10.1016/j.envint.2006.03.009.

Y. Icaga, “Fuzzy evaluation of water quality classification,” Ecological Indicators 7 (2007) 710. https://doi.org/10.1016/j.ecolind.2006.08.002.

S. Ramesh, “An innovative approach of Drinking Water Quality Index–A case study from Southern Tamil Nadu, India,” Ecological Indicators 10 (2010) 857. https://doi.org/10.1016/j.ecolind.2010.01.007.

R. Mirabbas, S. M. Mazloumzadeh and M. B Rahnama, “Evaluation of irrigation water quality using fuzzy logic,” Res. J. Environ. Sci., 2 (2008) 340. http://dx.doi.org/10.3923/rjes.2008.340.352.

APHA (American Public Health Association), “Standard methods for the examination of water and wastewater” American Public Health Association, Washington DC, USA, 1995, pp. 1467 https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1916575.

Sruthy Sajeev, Selvam Sekar, Binoj Kumar, Venkataraman Senapathi, Sang Yang Chung and Gnanachandrasamy Gopalakrishnan, “Variations of water quality deterioration based on GIS techniques in surface and groundwater resources in and around Vembanad Lake,Kerala,India,” Geochemitry 80 (2020), 125626. https://doi.org/10.1016/j.chemer.2020.125626.

L. A. Zadeh, “Fuzzy sets,” Inform. Control. 8 (1965) 338. https://doi.org/10.1016/S0019-9958(65)90241-X.

H. Gharib, M. H Sowlat, A. H. Mahvi, H. Mathmoudzadeh, H. Arabalibeik, M. Keshavarz, N.Karimzadeh, G. Hassani, “Development of a dairy cattle drinking water quality index(DCWQI) based on fuzzy inference systems,” Ecological Indicators 20 (2012) 228. https//:DOI:10.1016/j.ecolind.2012.02.015.

E. N. Nasibov, “Aggregation of fuzzy information on the basis of decompositional representation,” Cybernetics and system analysis 41 (2005) 309. https://doi.org/10.1007/s10559-005-0065-0.

A. Y. Sangodoyin, and K. Ogedengbe, “Surface water quality and quantity from the standpoint of irrigation and livestock,” Int. J. Environ. Stud. 38 (1991) 251. https://doi.org/10.1080/00207239108710670.

S. Ponsadailakshmi, S. Ganapathy Sankari, S. Mythili Prasanna, G. Madhurambal, “Evaluation of water quality suitability for drinking using drinking water quality index in Nagapattinam district, Tamil Nadu in Southern India,” Groundwater for sustainable development 6 (2018) 43. https://doi.org/10.1016/j.gsd.2017.10.005.

D. J. Rooney, K. W. Brown and J. C. Thomas, “The effectiveness of capillary barriers to hydraulically isolate salt contaminated soils,” Water, Air, and Soil Pollution 104 (1998) 403. https://doi.org/10.1023/A:1004966807950.

M. H. Sowlat, H. Gharibi, M. Yunesian, M. Tayefeh Mahmoudi, S. Lotfi, “A novel, fuzzy-based air quality index (FAQI) for air quality assessment, Atmospheric Environment,” 45 (2011) 2050. https://doi.org/10.1016/j.atmosenv.2011.01.060.

H. Gharib, M. H. Sowlat, A. H. Mahvi, H. Mathmoudzadeh, H. Arabalibeik, M. Keshavarz, N.Karimzadeh, G. Hassani. “Development of a dairy cattle drinking water quality index(DCWQI) based on fuzzy inference systems,” Ecological Indicators 20 (2012) 228. DOI:10.1016/j.ecolind.2012.02.015.

E. Shekariana, A. A. Gholizadeh, “Application of adaptive network-based fuzzy inference system method in economic welfare,” Knowledge-Based Systems 39 (2013) 151. https://doi.org/10.1016/j.procs.2016.09.435.

D. Kumar, J. Singh, O. P. Singh and Seema “A fuzzy logic-based decision support system for evaluation of suppliers in supply chain management practices,” Mathematical and Computer Modelling 57 (2013) 2945. https://doi.org/10.1504/IJLSM.2020.10041107.

F. Camastra, A. Ciaramella, V. Giovannelli, M. Lener, V. Rastelli, A. Staiano, G. Staiano and F. Starace, “A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference,” Expert Systems with Applications 42 (2015) 1710. https://doi.org/10.1016/j.eswa.2014.09.041.

E. Pourjavad, R. V. Mayorga, “A comparative study and measuring manufacturing system performance with Mamdani fuzzy inference system,” Journal of Intelligence Manufacturing 30 (2017) 1085. https://doi.org/10.1007/s10845-017-1307-5.

I. B. Turksen, “Measurement of membership functions and their acquisition,” Fuzzy Sets Syst. 40 (1991) 5. https://doi.org/10.1016/0165-0114(91)90045-R.

D. Park, A. Kandel and G. Langholz, “Genetic-based new fuzzy reasoning models with application to fuzzy control,” IEEE Trans. Syst. ManCybem., 24 (1994) 39. https://doi.org/10.1109/21.259684.

S. Ponsadai Lakshmi, R. Deepa , S. Ganapathy Sankari, and M. Jeyachandran, “Pollution Status of Groundwater Resources Through Hydrochemical Characteristics - A Case Study From Southern India,” Journal of the Nigerian Society of Physical Sciences 4 (2022) 751. https://doi.org/10.46481/jnsps.2022.751.

C. Gopi, A. Charles, C. Manivannan, S. Ponsadai Lakshmi, Adwin Jose and M. Muthiyan, “Physico-chemical and Trace metal Analysis in Ground water of Nagapattinam region in Nagapattinam district of Tamil Nadu state,” Journal of the Nigerian Society of Physical Sciences 5 (2023) 1160. https://doi.org/10.46481/jnsps.2023.1160.

Map of the sample collecting sites.

Published

2024-04-21

How to Cite

Mamdani-type fuzzy inference system for irrigational water quality. (2024). Journal of the Nigerian Society of Physical Sciences, 6(2), 1890. https://doi.org/10.46481/jnsps.2024.1890

Issue

Volume 6, Issue 2, May 2024

Section

Earth Sciences
Copyright & Licensing

Copyright (c) 2024 S. Ponsadai Lakshmi, C. Gopi, P. Adwin Jose

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mamdani-type fuzzy inference system for irrigational water quality. (2024). Journal of the Nigerian Society of Physical Sciences, 6(2), 1890. https://doi.org/10.46481/jnsps.2024.1890