Impact of recharge from a check dam on groundwater quality and assessment of suitability for drinking and irrigation purposes

Renganayaki, S. Parimala; Elango, L.

doi:10.1007/s12517-013-0989-z

Cited by 20 publications

(1 citation statement)

References 15 publications

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“…Some of these parameters and their related classification methods are: sodium percentage (Na%), Sodium Adsorption Ratio , Residual Sodium Carbonate (RSC), Electrical Conductivity (EC), Magnesium Ratio (MR), Potential Soil Salinity (PS), general soil classification or Kelly's ratios, pH, Corrosivity Rate (CR). Numerous studies attempted to examining or zoning groundwater quality for agriculture purposes by considering the relevant quality classification parameters (Bashir et al, 2013;Kumarasamy et al, 2014;Naseem et al, 2010;Oladeji et al, 2012;Parimala renganayaki and Elango, 2013;Ravikumar et al, 2015;Sheikhy Narany et al, 2014;Venkateswaran and Vediappan, 2013).…”

Section: Introductionmentioning

confidence: 99%

A new approach in qualitative zoning of groundwater to assessment suitable irrigation water using fuzzy logic spatial modeling via GIS

Tafreshi

2022

Preprint

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Water quality must be tested before it can be used for agricultural purposes since difficulties might arise in terms of increased soil salinity, decreased soil permeability, and decreased water absorption by plant roots; leading to reduced agricultural productivity. The aim of this study was to assessment suitable irrigation water based on a new approach in Asalem region, Iran. For reduce the uncertainty, fuzzy logic spatial modeling via GIS was applied. To receive these aims, four stages were performed. In stage 1, we calculate the values of nine conventional and effective parameters used for agricultural water quality classification upon analyzing the statistical quality data regarding various ions existing in 15 sample wells dug in 2015. In stage 2, interpolate these parameters by kriging method via ArcGIS software. In stage 3, parameter standardization with fuzzy membership function were done. And in stage 4, for Aggregation parameters, several fuzzy overlay operations were used. Finally, to identify the best operation, the correlations between the fuzzy membership and operation maps were used. Results showed that the "GAMMA 0.9" with six high correlation above 80%, is the best overly operation in our study area. According to the best operation map, only west and northwest parts of the study area have "good" to "excellent" groundwater quality for irrigation water and in other parts, the quality is within "poor" to "fair".

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