Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf

Mohammadi, Ali Akbar; Ghaderpoori, Mansour; Yousefi, Mahmood; Rahmatipoor, Malihe; Javan, Safoura

doi:10.15171/ehem.2016.23

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Magnesium oxide is an inexpensive catalyst with a high catalytic potential for degrading pollutants such as dyes or drugs [1][2][3]. Numerous studies have shown that magnesium oxide possesses high surface reactivity and strong surface alkalinity [4]. To improve the surface reactivity of magnesium oxide, its specific surface area can be increased by the preparation of nano-magnesium oxide or porous magnesium oxide.…”

Section: Introductionmentioning

confidence: 99%

Preparation of porous nano-magnesium oxide using a corn starch template method

Chen,

Mu,

Jiang

et al. 2024

J. Phys.: Conf. Ser.

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Nano-porous magnesium oxide was prepared using a corn starch template and a magnesium nitrate hexahydrate precursor, and characterized by TGA, XRD, FTIR, EDS, TEM, and BET surface area analysis. The results showed that porous magnesium oxide composed of nano-particles could be obtained using this method. The final magnesium oxide product was in the form of large flakes under low magnification, and the flakes were composed of small nano-scale particles under high magnification. The particle size of nano-porous magnesium oxide was 20–28 nm, the specific surface area was 29.37 m2/g, and the average pore diameter was 19 nm.

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Section: Introductionmentioning

confidence: 99%

Preparation of porous nano-magnesium oxide using a corn starch template method

Chen,

Mu,

Jiang

et al. 2024

J. Phys.: Conf. Ser.

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“…Their shared objective was to identify springs or areas of high productivity based on GIS data. Recent studies using neural networks to predict groundwater quality have estimated the concentrations of contaminants such as nitrate, fluoride, and salinity in the groundwater (Mohammadi et al., 2016; Sahour et al., 2020; Wagh et al., 2018). Despite successful estimation and mapping, the results did not target the specific end use of the resource or consider the groundwater quality in detail; instead, they set light criteria and focused on groundwater availability or concentration of a specific contaminant.…”

Section: Introductionmentioning

confidence: 99%

Delineation of groundwater quality locations suitable for target end‐use purposes through deep neural network models

et al. 2021

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Groundwater is the main source of water for beverages, and its quality varies depending on extraction location; this is particularly the case in regions with complex geology, topography, and multiple forms of land use. Thus, it is important to determine a suitable groundwater extraction location based on intended water use and the related water quality standards. In this study, deep neural network (DNN) models and GIS data relating to groundwater quality were applied to estimate potential maps of Gangwon Province in South Korea, where groundwater is frequently extracted for drinking purposes. These maps specify areas where the groundwater quality is conducive for being used as mineral water and water for brewing coffee (hereafter referred as "coffee water"). Sensitivity analysis identified how inputs were sensitive to model estimation and showed that land-use variables were the most sensitive. The importance of each variable quantified how good or bad its region is for the desired groundwater. The overall features of importance were similar between mineral water and coffee water. However, with differences in hydrogeological units, carbonate rock was a variable of high positive importance for mineral water; metamorphic rock was its equivalent for coffee water. Our results offer a potential map of desired groundwater quality in the absence of a detailed understanding of the underlying hydrochemical processes governing groundwater quality. Additionally, the development of such a potential mapping model can help to determine the appropriate development area of groundwater for their respective purposes. Abbreviations: DNN, deep neural network. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Measuring the parameters of soil and water is costly and time consuming. The prediction and simulation of these parameters using modeling, reduces the cost of water management (19). The HYDRUS-2D software has been developed to simulate the two-dimensional movement of water, heat and solvents in a porous medium and different humidity conditions.…”

Section: Introductionmentioning

confidence: 99%

Significance of physicochemical factors in the transmission of Escherichia coli and chloride

Akhavan

Ebrahimi

Navabian

et al. 2018

Environ Health Eng Manag

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Background: Organic manures are the source of many pathogenic bacteria which could be dangerous for human health. Bacterial transmission and retention in soil is important for processes ranging from contaminant degradation during in situ bioremediation to transport of pathogenic bacteria into groundwater. Methods: The aim of this study was to evaluate the transport of Escherichia coli and chloride (Cl) in the soil saturation conditions, considering the importance of preferential flow using synthetic macrospores (different diameters of macrospores including 0, 1 and 2 cm) and HYDRUS-2D model. Also, the effect of different salinity levels of water (including electrical conductivity of 1, 2 and 4 dSm-1) on the transmission of E. coli was investigated. The preferential flow system was prepared and designed using two sand sizes including fine sand and coarse sands in the columns. Results: The results showed that the retention of E. coli increased with the ionic strength of the solution, while the effect of E. coli in the fine sand was greater than that of the coarse sand. This bacterial transfer behavior was well described by numerical simulations. The importance of preferential flow in bacterial transfer showed that it increases at higher ionic strength, even if overall transmission is reduced. Although the bacterial transmission is facilitated in salinity, the results of this study showed that with increase of ionic strength of the soil solution, the amount of bacterial purification was increased that could be effective in controlling groundwater contamination with saline water management. Conclusion: According to the results, with increase of ionic strength of the soil solution, the amount of bacterial purification was increased that could be effective in controlling groundwater contamination with saline water management, so that the least transition has taken place and the conditions for the use of unconventional water sources were also created, without the environmental problem of the risk of groundwater pollution.

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Prediction and modeling of fluoride concentrations in groundwater resources using an artificial neural network: a case study in Khaf

Cited by 8 publications

References 18 publications

Preparation of porous nano-magnesium oxide using a corn starch template method

Preparation of porous nano-magnesium oxide using a corn starch template method

Delineation of groundwater quality locations suitable for target end‐use purposes through deep neural network models

Significance of physicochemical factors in the transmission of Escherichia coli and chloride

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