Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

Nejad, Samira Ghorbani; Falah, Fatemeh; Daneshfar, Mania; Haghizadeh, Ali; Rahmati, Omid

doi:10.1080/10106049.2015.1132481

Cited by 77 publications

(34 citation statements)

References 87 publications

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“…Recently, machine learning (ML) and soft computing techniques such as artificial intelligence have been successfully applied for the prediction of hazard and risk in environmental sciences (Choubin et al, 2017a(Choubin et al, , 2017bGhorbani Nejad et al, 2017;Choubin et al 2018b;Singh et al, 2018). However, the implementation of ML approaches for assessment of groundwater pollution risk are limited; and an integrated framework for groundwater risk assessment is still lacking.…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

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268

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This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80 percent were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.

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Section: Introductionmentioning

confidence: 99%

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

Self Cite

268

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“…The second dataset was the digital elevation model (DEM)-based dataset, including topographical factors [32]: elevation, gradient, aspect, relief amplitude (RA), surface roughness (SR), plan curvature (PLC), and profile curvature (PRC). However, having more factors does not necessarily mean a more complete and accurate landslide susceptibility map [13,21,33]. Due to being derived from the same DEM data source, the topographical factors were not independent of each other.…”

Section: Landslide Inventory Mapmentioning

confidence: 99%

“…2017, 6, 347 2 of 16 factors and landslides, not only avoid the dependence of mechanical models on high-precision data, but also reduce the subjectivity brought by expert evaluation statistical methods [8,9]. The statistical methods include the weighted liner combination model (WLC) [10,11], the logistic regression model [12][13][14], fuzzy synthetic evaluation model [15,16], and neural network model [17,18].…”

Section: Introductionmentioning

confidence: 99%

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

Chen

Han

Huang

et al. 2017

IJGI

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A landslide susceptibility map plays an essential role in urban and rural planning. The main purpose of this study is to establish a variable-weighted linear combination model (VWLC) and assess its potential for landslide susceptibility mapping. Firstly, different objective methods are employed for data processing rather than the frequently-used subjective judgments: K-means clustering is used for classification; binarization is introduced to determine buffer length thresholds for locational elements (road, river, and fault); landslide area density is adopted as the contribution index; and a correlation analysis is conducted for suitable factor selection. Secondly, considering the dimension changes of the preference matrix varying with the different locations of the mapping cells, the variable weights of each optimal factor are determined based on the improved analytic hierarchy process (AHP). On this basis, the VWLC model is established and applied to regional landslide susceptibility mapping for the Shennongjia Forestry District, China, where shallow landslides frequently occur. The obtained map is then compared with a map using the traditional WLC, and the results of the comparison show that VWLC is more reasonable, with a higher accuracy, and can be used anywhere that has the same or similar geological and topographical conditions.

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“…In some studies, data mining models such as frequency ratio (FR) (Oh et al, 2011;Davoodi Moghaddam et al, 2015), weights-of-evidence (WOE) (Ozdemir, 2011;Lee et al, 2012a;Pourtaghi and Pourghasemi, 2014), evidential belief function (EBF) (Nampak et al, 2014;Pourghasemi and Beheshtirad, 2014;Tahmassebipoor et al, 2015;Ghorbani Nejad et al, 2016), and certainty factor (CF) (Razandi et al, 2015) have been used for assessing groundwater potentiality. Furthermore, numerous studies have described the application of the logistic regression (LR) (Ozdemir, 2011;Pourtaghi and Pourghasemi, 2014), artificial neural network model (ANN) (Lee et al, 2012b), random forest , and analytical hierarchy process (AHP) (Adiat et al, 2012;Rahmati et al, 2014;Shekhar and Pandey, 2014) for preparing the GPM.…”

Section: Introductionmentioning

confidence: 99%

Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran

Rahmati

Melesse

2016

Science of The Total Environment

Self Cite

106

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Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

Cited by 77 publications

References 87 publications

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China

Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran

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