Development of irrigation water quality index incorporating information entropy

Singh, Kunwar Raghvendra; Goswami, Ankit Pratim; Kalamdhad, Ajay S.; Kumar, Bimlesh

doi:10.1007/s10668-019-00338-z

Cited by 39 publications

(11 citation statements)

References 19 publications

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“…The EWQII has been classi ed as very good quality (below 25), good quality (between 25 and 50), average quality (between 50 and 75) and poor quality (above 75) (Singh et al, 2019).…”

Section: Irrigation Water Quality Indicesmentioning

confidence: 99%

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Assessment of groundwater suitability for irrigational utilization determined by distinct quality indices and information entropy theory in the Indian Sundarban region

Molla

Rukhsana

2023

Preprint

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For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop production. The entropy information theory, several irrigational indices, multivariate statistics, GIS and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. 33 samples of groundwater have been collected in total, and they were evaluated for major cations, anions as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), Sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), Magnesium Hazard (MH) and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na+ ≥ Mg2+ ≥ Ca2+ ≥ K2+ and HCO3− ≥ Cl− ≥ NO3− ≥ SO42− ≥ F−, respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). As per the entropy-weighted water quality index for irrigation (EWQII), 60.60% of samples possess good to average quality, while the remaining 39.40% (poor) require severe restrictions before use in agricultural operations. As a result, to assure sustainable agricultural development, in the research area, continuous monitoring and water resource management are required.

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“…The EWQII has been classi ed as very good quality (below 25), good quality (between 25 and 50), average quality (between 50 and 75) and poor quality (above 75) (Singh et al, 2019).…”

Section: Irrigation Water Quality Indicesmentioning

confidence: 99%

“…The SSP is a crucial measure for grading irrigation water according to soil permeability (Singh et al 2019). Groundwater containing SSP levels less than 60 īs considered good for irrigation; however, levels greater than 60 are unsuitable for irrigation (Todd 1959).…”

Section: Variation Of Irrigation Water Quality Indices For Crop Growt...mentioning

confidence: 99%

Assessment of groundwater suitability for irrigational utilization determined by distinct quality indices and information entropy theory in the Indian Sundarban region

Molla

Rukhsana

2023

Preprint

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“…Karbassi et al (2011), for example, created a WQI for the Gorganrood River based on a variety of river conditions and features, including geographical, hydrological, discharge rate, and pollution sources. Additionally, Singh et al (2020) evaluated and studied the water quality of the Bharalu river using yearly datasets for its suitability for irrigation using the suggested entropy weighted for spatiotemporal variability. Furthermore, Parvez & Inayathulla (2020) developed a WQI for the upper Cauvery, Karnataka, India, by measuring five water quality parameters: pH, nitrates, chloride, EC, and fluoride.…”

Section: Introductionmentioning

confidence: 99%

Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

et al. 2022

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The primary objective of this study was to calculate and assess the irrigation water quality index. Furthermore, an effective method for predicting IWQI using artificial neural network (ANN) and multiple linear regression (MLR) models was proposed. The accuracy performance of each model was evaluated at the end of this paper. According to the calculated index based on 49 groundwater samples, the Sidi El Hani aquifer was of good and sufficient quality. Moreover, both the ANN and MLR models performed well in terms of actual and predicted water quality. The ANN model, on the other hand, demonstrated the highest prediction accuracy. The results of this model also revealed that the predicted and computed values were close, with determination coefficients R2, RMSE, and MAE of about 0.95, 1.02, and 0.90, respectively. As a result, the proposed ANN model in this study was consistent and sufficient. These findings will help to guide irrigation water management decisions for the study aquifer in the future. The proposed ANN model can also be used to estimate the irrigation water index of other semi-arid aquifers, but accuracy is dependent on proper training techniques and selection parameters.

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“…Similarly, Misaghi et al (2017) introduced a new IWQI for irrigation uses based on the NSFWQI adopted by FAO 29 guidelines and applied this index to the Iranian Ghezel Ozan urban River. However, the main disadvantage is that weights were taken from the Delphi method based on expert opinion, which led to uncertainty and biases of the environmental problem in different steps of the decision-making process (Islam et al 2017a;2020c;Singh et al 2020;Matta et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Statistics and Entropy Theory for Irrigation Water Quality and Entropy-Weighted Index Development in a Subtropical Urban River, Bangladesh

Siddique

Islam

Hossain

et al. 2021

Preprint

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Currently, a well-developed combination of irrigation water quality index (IWQIs) and entropy water quality index (EWQIs) for surface water appraisal in a polluted subtropical urban river is very scarce in the literature. To close this gap, we developed IWQIs by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed EWQIs based on information entropy in the Dhaleshwari River, Bangladesh. Fifty surface water samples were collected from five sampling locations during the dry and wet seasons and analyzed for sixteen variables. Principal component analysis (PCA), factor analysis (FA), Moran’s spatial autocorrelation, and random forest (RF) model were employed in the datasets. Weights were allocated for preliminary variables to compute IWQI-1, 2 and EWQI-1, 2 respectively. The resultant IWQIs showed an analogous trend with EWQIs and revealed poor to good quality water, with IWQI-1 for the dry season and IWQI-2 for the wet season is further suggested. The entropy theory recognized that Mg, Cr, TDS, and Cl- for the dry season and Cd, Cr, Cl- and SO42- for the wet season are the major contaminants that affect irrigation water quality. The primary input variables were lessened to ultimately shortlisted ten variables, which revealed good performance in demonstrating water quality status since weights have come effectively from PCA than FA. The results of the RF model depict NO3-, Mg, and Cr as the most predominant variables influencing surface water quality. A significant dispersed pattern was detected for IWQImin-3 in the wet season (Moran’s I>0). Overall, both IWQIs and EWQIs will generate water quality control cost-effective, completely objective to establish a scientific basis of sustainable water management in the study basin.

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Development of irrigation water quality index incorporating information entropy

Cited by 39 publications

References 19 publications

Assessment of groundwater suitability for irrigational utilization determined by distinct quality indices and information entropy theory in the Indian Sundarban region

Assessment of groundwater suitability for irrigational utilization determined by distinct quality indices and information entropy theory in the Indian Sundarban region

Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

Multivariate Statistics and Entropy Theory for Irrigation Water Quality and Entropy-Weighted Index Development in a Subtropical Urban River, Bangladesh

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