Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique

Chaudhry, Akshay Kumar; Kumar, Kamal; Alam, M. A.

doi:10.1080/10106049.2019.1695959

Cited by 62 publications

(24 citation statements)

References 38 publications

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“…They are represented by siwaliks and alluvium deposits. Soil consists predominantly of four main types of soil, namely, ustochrepts, ustorthents, ustipsamments, and ustifluvents (Chaudhry et al 2019b). Based on the piper trilinear plot, it was found that the governing cations and anions in the region are calcium (Ca 2+ ), sodium (Na + ), magnesium (Mg 2+ ), and bicarbonate (HCO3 -) [Figure 2S].…”

Section: Study Areamentioning

confidence: 99%

Groundwater Fluoride Contamination and Probabilistic Health-risk Assessment in Fluoride Endemic Areas of the Lower Satluj Basin, Punjab, India

Chaudhry¹,

Sachdeva²

2021

Preprint

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Groundwater fluoride contamination in some parts of the study region was long recognized as a water quality issue. The results of WQI showed that 85% and 15% of sampling sites fall within the class of “good” and “poor” quality, respectively. Non-carcinogenic health risk (NCHR) assessment (using Hazard quotient (HQ)), and sensitivity analysis for three age groups were also carried out using the Monte Carlo simulation technique. The estimated levels of HQoral were greater in magnitude than those estimated from HQdermal, thus the main source for fluoride toxicity is oral ingestion. Amongst the three age groups studied, children >female >male were found to be more prone to NCHR with HQoral ranging from (0.13-5.45), (0.07-2.97), and (0.06-2.51), respectively. Sensitivity analyses indicated that fluoride concentration, ingestion rate, shower exposure time, and fractional skin contact with water were the most relevant variables in the model to reduce the potential health effect.

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Section: Study Areamentioning

confidence: 99%

Groundwater Fluoride Contamination and Probabilistic Health-risk Assessment in Fluoride Endemic Areas of the Lower Satluj Basin, Punjab, India

Chaudhry¹,

Sachdeva²

2021

Preprint

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“…Logika fuzzy dapat dikombinasikan dengan Analytical Hierarchy Proses (AHP) dan dikenal dengan Fuzzy AHP (FAHP). FAHP telah digunakan dalam penentuan potensi lokasi penggurunan (Kacem et al, 2019), pemodelan erosi (Haidara, Tahri, Maanan, & Hakdaoui, 2019;Saha, Gayen, Pourghasemi & Tiefenbacher, 2019) dan potensi air tanah (Chaudhry, Kumar, & Alam, 2019).…”

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FUZZY ANALYTIC HIERARCHY PROCESS BERBASIS MORFOMETRI UNTUK PRIORITAS PENANGANAN EROSI DI SUB DAERAH ALIRAN SUNGAI OYO (Fuzzy analytic hierarchy process based on watershed morphometry for erosion priority mapping in Oyo Sub Watershed)

Khasanah

Miardini²

2020

JPPDAS

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Erosion is an indication of watershed degradation. In a watershed management, it is necessary to prioritize the handling that takes into account the characteristics of the watershed, one of which is morphometric character. This study aims to determine the priority location of erosion management in Oyo Watershed based on morphometric data using Fuzzy AHP modeling. Morphometric parameters that affect erosion are Rbm (bifurcation ratio), Rc (circulatory ratio), Dd (drainage density), T (texture), Su (Gradient) and Rn (Rugness Number). The highest value of the output shows the priority location that should be controlled. The high priority levels are found in 21 sub-watersheds with an area of 3,82 ha, medium levels are in 35 sub-watersheds with an area of 17,780.21 ha, low levels are in 106 sub-sub Watersheds with an area of 48,974.46 ha. The priority order for erosion management at the sub-watershed level is very important to prepare a watershed management plan in order to control soil erosion that is appropriate to protect the soil from further erosion.

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“…Additionally, FL acts as an important method for transmitting outcomes in a very clear language format (also known as linguistic variables) to the public and beneficiaries. Several studies have used FL in assessing the quality of groundwater (Abbasnia et al, 2018; Chaudhry et al, 2019; Liu et al, 2021) and have suggested addressing complex water quality issues. All these studies implemented the FL GIS‐based GQI (FGQI) technique using point‐based short‐term data (Dahiya et al, 2007; Selvaraj et al, 2020; Vadiati et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

Chaudhry

Sachdeva

2021

Water & Environment J

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This study proposes the evaluation of GIS‐based groundwater quality index (GQI) and spatial variability with a hybrid system that combines Fuzzy Logic with GIS‐based GQI. A case study in the semi‐arid area of Punjab, namely Rupnagar, illustrates the proposed hybrid outline. Two GIS‐based GQI models GQI‐10 (using ten water quality parameters (WQPs)) and GQI‐6 (using six “apprehensive/crucial” WQPs), as well as hybrid Fuzzy Logic GIS‐based GQI (FGQI) (using six crucial WQPs) models, were developed using trapezoidal membership functions and rule sets based on expert's knowledge. Performance comparison of GQI models showed that the FGQI model predicts improved groundwater quality than the GQI‐10 and GQI‐6. FGQI model variance, RMSE, and R2 values are 84.25, 6.49, 0.89, respectively. Results have shown that the developed FGQI model is successful and GQI modelling suggests the use of northern and central groundwater without treatment to be unsafe for drinking purposes.

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Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique

Cited by 62 publications

References 38 publications

Groundwater Fluoride Contamination and Probabilistic Health-risk Assessment in Fluoride Endemic Areas of the Lower Satluj Basin, Punjab, India

Groundwater Fluoride Contamination and Probabilistic Health-risk Assessment in Fluoride Endemic Areas of the Lower Satluj Basin, Punjab, India

FUZZY ANALYTIC HIERARCHY PROCESS BERBASIS MORFOMETRI UNTUK PRIORITAS PENANGANAN EROSI DI SUB DAERAH ALIRAN SUNGAI OYO (Fuzzy analytic hierarchy process based on watershed morphometry for erosion priority mapping in Oyo Sub Watershed)

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

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