An investigation of the potential environmental contamination from the leachate of the Rasht waste disposal site in Iran

Nakhaei, Mohammad; Amiri, Vahab; Rezaei, Khalil; Moosaei, Firouz

doi:10.1007/s10064-014-0577-9

Cited by 36 publications

(7 citation statements)

References 17 publications

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“…This study area also falls under gneisses and associated rocks, older alluvium, and older alluvium with lateritic types of aquifer media. The older alluvium has moderate to high yield prospects, where older alluvium with laterite rocks has adverse or limited yield prospects within 100 to 700 gpd/ft 2 hydraulic conductivity in the study area (Nakhaei et al, 2015;Thapa et al, 2018).…”

Section: Study Areamentioning

confidence: 93%

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

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Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

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Section: Study Areamentioning

confidence: 93%

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

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show abstract

“…The methods and/or equipment used to perform the physico-chemical analyzes and references for previous use of these methods are as follows: The hardness was measured by titrimetry ( Sunitha, 2014 ). Temperature, pH, electrical conductivity (EC) are measured by a portable multi-parameter meter with probe WTW340i ( Nakhaei et al., 2015 ; Amiri et al., 2017 ; Hounsinou, 2021 ). Turbidity was measured by a colorimeter, TDS was measured by a conductimeter ( Hounsinou, 2020 ; Hounsinou, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Use of graphical and multivariate statistical methods to show a marine intrusion and salinization of a coastal water table: case study of the township of Abomey-Calavi, Benin

Hounsinou

2022

Heliyon

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“…The concentrations of NO 3 − in all the samples were measured very low as compared with its allowable limit. The reason for such results is that the water samples were collected from high-altitude glacier-fed channels, where the possible source of NO 3 − could be the dissolution | 1179 of salts from glacial deposits (Nakhaei et al, 2015). No potential source of NO 3 − pollutants was found in the vicinity of contemplated water resources.…”

Section: Physico-chemical Parameters Of Water Qualitymentioning

confidence: 99%

Evaluation and classification of water quality of glacier‐fed channels using supervised learning and water quality index

Ahmed

Waqas

Khan

et al. 2021

Water & Environment J

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Potable water is considered the smallest fraction of total water on the earth's surface. It is being depleted and getting polluted with the rapid growth of the world's population (Kulshreshtha, 1998). According to a report published in 2008, the water scarcity level has been increased to a drastic level, i.e,. in some regions of the world the water supply has been decreased by 50% than its demand (MDG Gap Task Force, 2008). The consumption of safe drinking water is the basic need and a rudimentary right of human beings. Unfortunately, in the developing world, governments are not strictly implementing environmental protection laws; therefore, over 1 billion people are using unclean and contaminated water to meet their domestic needs (WHO, 2004).High-altitude surface water resources including glacial-fed lakes, ponds, channels, etc. are the major sources of potable water for the local communities, tourists, trekkers, and wildlife. In the mountainous terrain, the geomorphological and hydrogeological systems play an important role in governing the amount of discharge of snow melting water (UNCSD, 2012

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An investigation of the potential environmental contamination from the leachate of the Rasht waste disposal site in Iran

Cited by 36 publications

References 17 publications

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Use of graphical and multivariate statistical methods to show a marine intrusion and salinization of a coastal water table: case study of the township of Abomey-Calavi, Benin

Evaluation and classification of water quality of glacier‐fed channels using supervised learning and water quality index

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