Groundwater quality assessment for irrigation purposes based on irrigation water quality index and its zoning with GIS in the villages of Chabahar, Sistan and Baluchistan, Iran

Abbasnia, Abbas; Mahvi, Amir Hossein; Nabizadeh, Ramin; Yousefi, Mahmood; Soleimani, Hamed; Alimohammadi, Mahmood

doi:10.1016/j.dib.2018.05.061

Cited by 114 publications

(57 citation statements)

References 10 publications

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“…As shown in Table 2 , in the number of neurons greater than the optimal number of neurons, 10, the mean square error (MSE) was not significantly altered. Therefore, all the modeling steps were done based on the number of neurons 10 and the Lewenberg-Markow algorithm to predict the sodium absorption ratio [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

et al. 2018

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In this article the data of the groundwater quality of Aras catchment area were investigated for estimating the sodium absorption ratio (SAR) in the years 2010–2014. The artificial neural network (ANN) is defined as a system of processor elements, called neurons, which create a network by a set of weights. In the present data article, a 3-layer MLP neural network including a hidden layer, an input layer and an output layer had been designed. The number of neurons in the input and output layers of network was considered to be 4 and 1, respectively, due to having four input variables (including: pH, sulfate, chloride and electrical conductivity (EC)) and only one output variable (sodium absorption ratio). The impact of pH, sulfate, chloride and EC were estimated to be 11.34%, 72.22%, 94% and 91%, respectively. ANN and multiple linear regression methods were used to estimate the rate of sodium absorption ratio of groundwater resources of the Aras catchment area. The data of both methods were compared with the model׳s performance evaluation criteria, namely root mean square error (RMSE), mean absolute error (%) and correlation coefficient. The data showed that ANN is a helpful and exact tool for predicting the amount SAR in groundwater resources of Aras catchment area and these results are not comparable with the results of multiple linear regressions.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

et al. 2018

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“…Iranshahr city is located in Sistan and Baluchistan province of Iran between the latitudes 27°12′ N and Longitudes 60° 41′ E. According to the demographic information of Iran, this city encompassed with an area 30,200 km 2 . Iranshahr city has a warm and dry climate with an annual mean temperature of 26.5 °C [8] , [9] , [10] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on health risk assessment of fluoride in water distribution network of Iranshahr, Iran

et al. 2018

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The main of this data was determine the concentrations and health risks of fluoride in 66 drinking water samples collected from villages of the Iranshahr city, Sistan and Baluchestan Province in Iran. Fluoride concentration was measured by the standard SPADNS method. Data indicated that fluoride concentration in drinking water ranged from 0.25 to 1.72 mg L−1 and average of fluoride concentration was 0.27 mg L−1. The mean estimated daily intake (EDI) values for fluoride in different groups of infants, children, teenagers and adults were 0.0021, 0.0151, 0.0107 and 0.0086 mg/kg, respectively. Also, risk assessment data indicated that hazard quotient (HQ) value of groundwater samples is more than 1 in 6% of groundwater samples in age groups of children and teenagers.

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“…According to the results obtained by the researchers, more than 56% of the groundwater resources in the study area are not suitable (SR: serious restriction) and 44% very weak (HR: high restriction). Abbasnia et al (2018) evaluated groundwater samples in terms of IWQI in the Chabahar, Sistan and Baluchistan regions of Iran: 40% of the groundwater samples are in the perfect category (NR: no restriction), while 60% are in the good (LR: low restriction) category. Brhane (2016) classified the groundwater resources in the study area (Adigrat town/Ethiopia) as medium restriction (90.1%) and high limitation (8.9%) in terms of GIS-based IWQI values and generated spatial distribution maps for the relevant classifications.…”

Section: Iwqi and Spatial Analysismentioning

confidence: 99%

Evaluation for Irrigation Water Purposes of Groundwater Quality in the Vicinity of Sivas City Centre (Turkey) by Using Gis and an Irrigation Water Quality Index

Karakus

Yıldız

2019

Irrigation and Drainage

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In this study, the physical and chemical characteristics of wet and dry season groundwater quality parameters of 35 wells within the provincial boundaries of Sivas were used for calculations such as sodium adsorption ratio (SAR), Kelly's ratio (KR), sodium percentage (Na%), permeability index (PI), residual sodium carbonate (RSC) and magnesium hazard (MH), which are used in irrigation water quality ratings. Also, the irrigation water quality index (IWQI) was calculated for all groundwater sources within the study area. Groundwater quality parameters and the spatial distribution of the parameters calculated throughout the study were mapped using GIS, and irrigation water quality evaluation was performed in accordance with USA Salinity Laboratory diagram and irrigation water quality standards. The correlations between the irrigation water quality parameters were determined using Pearson correlation analysis. IWQI values showed that the majority of groundwater samples (45.7%) are in the ‘weak' (MR: medium rating) category for the dry season, whereas 42.9% of groundwater samples fall within the ‘very weak' (HR: high restriction) category for the wet season. © 2019 John Wiley & Sons, Ltd.

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Groundwater quality assessment for irrigation purposes based on irrigation water quality index and its zoning with GIS in the villages of Chabahar, Sistan and Baluchistan, Iran

Cited by 114 publications

References 10 publications

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

Data on health risk assessment of fluoride in water distribution network of Iranshahr, Iran

Evaluation for Irrigation Water Purposes of Groundwater Quality in the Vicinity of Sivas City Centre (Turkey) by Using Gis and an Irrigation Water Quality Index

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