Prediction of Lateral Load Capacity of Pile in Clay Using Multivariate Adaptive Regression Spline and Functional Network

2015 ◽  
Vol 40 (6) ◽  
pp. 1565-1578 ◽  
Author(s):  
Sarat Kumar Das ◽  
Shakti Suman
Keyword(s):  
Load Capacity ◽  
Lateral Load ◽  
Multivariate Adaptive Regression Spline ◽  
Functional Network ◽  
Regression Spline ◽  
Adaptive Regression

Lateral Load Capacity of Piles in Clay Using Genetic Programming and Multivariate Adaptive Regression Spline

2015 ◽  
Vol 45 (3) ◽  
pp. 349-359 ◽  
Author(s):  
Pradyut Kumar Muduli ◽  
Manas Ranjan Das ◽  
Sarat Kumar Das ◽  
Swagatika Senapati
Keyword(s):  
Genetic Programming ◽  
Load Capacity ◽  
Lateral Load ◽  
Multivariate Adaptive Regression Spline ◽  
Regression Spline ◽  
Adaptive Regression

Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

2020 ◽  
Vol 26 (2) ◽  
pp. 185-200
Author(s):  
Said Benchelha ◽  
Hasnaa Chennaoui Aoudjehane ◽  
Mustapha Hakdaoui ◽  
Rachid El Hamdouni ◽  
Hamou Mansouri ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Multivariate Adaptive Regression Spline ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Neural Network Models ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

Sign in / Sign up

Export Citation Format

Share Document