Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Falah, Fatemeh; Nejad, Samira Ghorbani; Rahmati, Omid; Daneshfar, Mania; Zeinivand, Hossein

doi:10.1080/10106049.2016.1188166

Cited by 75 publications

(26 citation statements)

References 76 publications

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“…They concluded that RF performed best with an ROC of 84.6%, followed by the SVM and BRT models. Falah et al [27] tested the applicability of Generalized Additive Model (GAM) using 12 groundwater conditioning factors based on 6439 spring locations for training and evaluation. When they compared their work with three bivariate statistical models, GAM performed slightly better than Weights-of-Evidence (WOE) with AUC value of 77% against 76.3%.…”

Section: Introductionmentioning

confidence: 99%

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods-Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance-to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

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Section: Introductionmentioning

confidence: 99%

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Kalantar

Al-Najjar

Pradhan

et al. 2019

Water

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“…This model was introduced by Hastie and Tibshirani () as an extension of the generalized linear model, without the assumption of linearity in the relationship between predictors and the predictand, as well as relaxing the normality assumption. Even if only few applications of the GAM in stream temperature have been reported (Wehrly et al ; Laanaya et al ), it has been widely used in hydrology (Chebana et al ; Zhang et al ; Falah et al ; Iddrisu et al ; Rahman et al ). GAM is defined as follows:

g (E false(y false) = f_{1} (x_{1}) + f_{2} (x_{2}) + \dots + f_{p} (x_{p}) + ε,

where g is the link function, E ( y ) is the expected value of the predictand (in our case, the daily mean stream temperature), x j is the j th predictor, f j is the associated smooth nonlinear function (often combination of cubic splines), and ε is the error term.…”

Section: Methodsmentioning

confidence: 99%

“…This model was introduced by Hastie and Tibshirani (1990) as an extension of the generalized linear model, without the assumption of linearity in the relationship between predictors and the predictand, as well as relaxing the normality assumption. Even if only few applications of the GAM in stream temperature have been reported (Wehrly et al 2009;Laanaya et al 2017), it has been widely used in hydrology (Chebana et al 2014;Zhang et al 2015;Falah et al 2017;Iddrisu et al 2017;Rahman et al 2018). GAM is defined as follows:…”

Section: Generalized Additive Modelmentioning

confidence: 99%

Stream Temperature Modeling Using Functional Regression Models

Boudreault

Bergeron

St‐Hilaire

et al. 2019

J American Water Resour Assoc

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Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.

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“…Several researchers have used TWI for groundwater potentiality assessment (Pourghasemi and Beheshtirad, 2014;Pourtaghi and Pourghasemi, 2014;Falah et al, 2016). According to Rodhe and Seibert (1999) and Davoodi Moghaddam et al (2015), the topography has a decisive role in the spatial variation of hydrological conditions (e.g.…”

Section: Topographic Wetness Index (Twi)mentioning

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

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107

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Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods

Cited by 75 publications

References 76 publications

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Stream Temperature Modeling Using Functional Regression Models

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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