scholarly journals Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

2017 ◽  
Vol 9 (7) ◽  
pp. 1157 ◽  
Author(s):  
Soyoung Park ◽  
Se-Yeong Hamm ◽  
Hang-Tak Jeon ◽  
Jinsoo Kim
Keyword(s):  
Logistic Regression ◽  
Groundwater Potential ◽  
Multivariate Adaptive Regression Spline ◽  
Regression Spline ◽  
Spline Models ◽  
Potential Mapping ◽  
Adaptive Regression ◽  
Groundwater Potential Mapping

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.

scholarly journals Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility

2020 ◽  
Vol 12 (20) ◽  
pp. 3284
Author(s):  
Paramita Roy ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Rabin Chakrabortty ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
River Basin ◽  
Cross Validation ◽  
Gully Erosion ◽  
Multivariate Adaptive Regression Spline ◽  
Boosted Regression Tree ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Fold Cross Validation ◽  
Very High

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.

scholarly journals Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

2020 ◽  
Vol 10 (7) ◽  
pp. 2469 ◽  
Author(s):  
Phong Tung Nguyen ◽  
Duong Hai Ha ◽  
Mohammadtaghi Avand ◽  
Abolfazl Jaafari ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Soft Computing ◽  
Predictive Value ◽  
Flow Direction ◽  
Groundwater Potential ◽  
Topographic Wetness Index ◽  
Ensemble Techniques ◽  
Well Yield ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

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