Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression

Zhang, Wengang; Goh, Alvina

doi:10.12989/gae.2016.10.3.269

Cited by 51 publications

(9 citation statements)

References 40 publications

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“…In order to The endpoints of the segments of each sub-region are called knots (Figure 3). The resulting piecewise regression lines (basis functions, BFs) make the final regression form flexible and appropriate for capturing trends from linear functions as bellow [51]:…”

Section: Multivariate Regression Spline (Mars)mentioning

confidence: 99%

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Zounemat‐Kermani

Keshtegar

Kisi

et al. 2021

Water

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This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

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Section: Multivariate Regression Spline (Mars)mentioning

confidence: 99%

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Zounemat‐Kermani

Keshtegar

Kisi

et al. 2021

Water

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“…Lee and Chen, 2005;Lee et al, 2006), geology (e.g. Zhang and Goh, 2016;Zhang et al, 2015), energy (e.g. Li et al, 2016;Roy et al, 2018) and hydrology (e.g.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A cold-health watch and warning system, applied to the province of Quebec (Canada)

Yan

Chebana

Masselot

et al. 2020

Science of The Total Environment

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“…Lee and Chen, 2005;Lee et al, 2006), geology (e.g. Zhang and Goh, 2016;Zhang et al, 2015), energy (e.g. Li et al, 2016;Roy et al, 2018) and hydrology (e.g.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines

Msilini¹,

Masselot²,

Ouarda³

2020

Journal of Hydrometeorology

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Hydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account non-linearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.

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Evaluating seismic liquefaction potential using multivariate adaptive regression splines and logistic regression

Cited by 51 publications

References 40 publications

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

A cold-health watch and warning system, applied to the province of Quebec (Canada)

Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines

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