Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system
Groundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing the groundwater suitableness for irrigation by employing Mamdani fuzzy inference system (MFIS). The groundwater quality data of premonsoon (PRM) and postmonso...
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Elsevier
2024-03-01
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author | Sankar Loganathan Devananth Ramakrishnan Mahenthiran Sathiyamoorthy Hazi Mohammad Azamathulla |
author_facet | Sankar Loganathan Devananth Ramakrishnan Mahenthiran Sathiyamoorthy Hazi Mohammad Azamathulla |
author_sort | Sankar Loganathan |
collection | DOAJ |
description | Groundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing the groundwater suitableness for irrigation by employing Mamdani fuzzy inference system (MFIS). The groundwater quality data of premonsoon (PRM) and postmonsoon (POM) seasons were used for this purpose. Based on the mean value, the dominant cations were in the sequence of sodium > magnesium > calcium > potassium and the dominant anions order was bicarbonate > chloride > sulphate > nitrate. The Gibbs diagram showed that the major ionic concentration of groundwater was primarily influenced by rock-water interaction and the evaporation process. To determine groundwater suitability for irrigation purposes, sodium adsorption ratio, electrical conductivity, sodium percentage, residual sodium carbonate, magnesium ratio and Kelly's ratio were estimated. Then a Mamdani fuzzy inference system was developed with three input and one output variable. The fuzzy output categorized sample as “good” (PRM-36%, POM-44%) “good to permissible” (PRM-7%, POM-2%), “permissible” (PRM-24%, POM-23%), “permissible to poor” (PRM-5%, POM-4%) and “poor” (PRM-28%, POM-27%). On comparing the output of conventional irrigation water quality classification method, the developed model was found to be more accurate than the conventional method for classifying the irrigation water suitability. This model will assist decision-makers to develop strategies for the long-term management of groundwater resources in the study area. |
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language | English |
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spelling | doaj.art-0008682162724673926ecbf77f25019d2024-03-24T07:00:40ZengElsevierResults in Engineering2590-12302024-03-0121101789Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference systemSankar Loganathan0Devananth Ramakrishnan1Mahenthiran Sathiyamoorthy2Hazi Mohammad Azamathulla3School of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, IndiaSchool of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, IndiaSchool of Civil Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India; Corresponding author.Department of Civil Engineering, University of the West Indies, St. Augustine, Trinidad and TobagoGroundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing the groundwater suitableness for irrigation by employing Mamdani fuzzy inference system (MFIS). The groundwater quality data of premonsoon (PRM) and postmonsoon (POM) seasons were used for this purpose. Based on the mean value, the dominant cations were in the sequence of sodium > magnesium > calcium > potassium and the dominant anions order was bicarbonate > chloride > sulphate > nitrate. The Gibbs diagram showed that the major ionic concentration of groundwater was primarily influenced by rock-water interaction and the evaporation process. To determine groundwater suitability for irrigation purposes, sodium adsorption ratio, electrical conductivity, sodium percentage, residual sodium carbonate, magnesium ratio and Kelly's ratio were estimated. Then a Mamdani fuzzy inference system was developed with three input and one output variable. The fuzzy output categorized sample as “good” (PRM-36%, POM-44%) “good to permissible” (PRM-7%, POM-2%), “permissible” (PRM-24%, POM-23%), “permissible to poor” (PRM-5%, POM-4%) and “poor” (PRM-28%, POM-27%). On comparing the output of conventional irrigation water quality classification method, the developed model was found to be more accurate than the conventional method for classifying the irrigation water suitability. This model will assist decision-makers to develop strategies for the long-term management of groundwater resources in the study area.http://www.sciencedirect.com/science/article/pii/S2590123024000422GroundwaterHydrogeochemistryGibbs diagramIrrigation water quality indexMamdani fuzzy inference system |
spellingShingle | Sankar Loganathan Devananth Ramakrishnan Mahenthiran Sathiyamoorthy Hazi Mohammad Azamathulla Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system Results in Engineering Groundwater Hydrogeochemistry Gibbs diagram Irrigation water quality index Mamdani fuzzy inference system |
title | Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system |
title_full | Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system |
title_fullStr | Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system |
title_full_unstemmed | Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system |
title_short | Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system |
title_sort | assessment of irrigational suitability of groundwater in thanjavur district southern india using mamdani fuzzy inference system |
topic | Groundwater Hydrogeochemistry Gibbs diagram Irrigation water quality index Mamdani fuzzy inference system |
url | http://www.sciencedirect.com/science/article/pii/S2590123024000422 |
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