Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Kalantar, Bahareh; Pradhan, Biswajeet; Naghibi, Seyed Amir; Motevalli, Alireza; Mansor, Shattri

doi:10.1080/19475705.2017.1407368

Cited by 438 publications

(184 citation statements)

References 74 publications

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“…Consequently, MDA seems to be a reliable approach for groundwater potential mapping. Although ROC-AUC is one of the most common indices to evaluate prediction models and was frequently used by [6,23,39,40,[65][66][67][68] for groundwater potential mapping validation, our future research will further investigate the accuracy of MDA model for GPM using other validation methods. Moreover, we note, however, that the accuracy of the groundwater/springs inventory dataset has a significant effect on the validity and accuracy assessment of GPMs [37].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, we note, however, that the accuracy of the groundwater/springs inventory dataset has a significant effect on the validity and accuracy assessment of GPMs [37]. To improve accuracy, a variety of sources, such as field survey, remote sensing imagery, and aerial photos can be used to prepare a spring inventory map [68] and locate groundwater discharge [69], which can lead to a complete and accurate inventory map, even in inaccessible, steep, and high altitude areas.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…Altitude is an influential factor among the various landslide explanatory variables, because it is affected by several geomorphologic and geological processes. Slope, which can be described as the form between any section of the surface and a horizontal datum, has considerable influence on slope stability [33]. The degree of vulnerability to landslides may differ based on slope direction, because the water content of the surface, vegetation type, and soil strength may be different.…”

Section: Topography Factorsmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

2019

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This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning. deforestation, increased urbanization, and an increase in regional precipitation in landslide-prone areas due to climate change [5]. It is essential that both susceptible and stable areas be identified to mitigate property damage, environmental degradation, and loss of life. Consequently, landslide susceptibility assessments, i.e., assessments of the spatial probability of a landslide occurring, are a huge step forward in the comprehensive hazard management of landslides [6,7]. The landslide susceptibility map (LSM) produced by a landslide susceptibility assessment can be a useful tool for authorities with decision-making capabilities.Many methods and techniques have been proposed to evaluate landslide susceptibility. In the past few decades, statistical approaches have become popular in the use of remote sensing (RS) with a geographic information system (GIS). There are many statistical approaches used in landslide susceptibility assessment, including a frequency ratio (FR) [8,9], certainty factor (CF) [10], statistical index (SI) [11,12], as well as weight of evidence (WoE) [7,13,14] and logistic regression (LR) [15,16] approaches.Recently, machine learning techniques have become popular in various fields. Machine learning, a branch of artificial intelligence, uses computer algorithms to analyze and predict information based on learning from training data [17,18]. Due to its robustness and high generalization capability, the use of machine learning has increased in landslide susceptibility analysis. Among the machine learning methods, artificial neural network [19,20], fuzzy logic [21,22], neuro-fuzzy [23], support vector machine [24,25], random forest [26,27], and naïve Bayes tree [17,28] methods have been popularly applied.More recently, ensemble machine l...

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“…Conditional factors for describing morphology such as elevation, slope and aspect have proven particularly effective in predicting the spatial distribution of geological hazards (Fabbri et al, 2003), so in this study, the relationship between hazard-related common factors such as elevation, slope and aspect and geological hazards were considered. Elevation, slope, and aspect are the typical variables used to describe morphology (Kalantar et al 2017) and always obtained from the DEM data. In this study, DEM data with resolution of 30 m × 30 m was derived from the National Basic Science Data Sharing Service Platform, Chinese Academy of Sciences (http://www.gscloud.cn).…”

Section: Predisposing Factorsmentioning

confidence: 99%

Fractal-Based Spatial Distribution Analysis of Geological Hazards and Measurement of Spatial Association With Hazard-Related Predisposing Factors

Zhou

Wang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Fractal model as an effective solution to complex nonlinear problems or phenomena has been widely used to describe such complicated phenomenon as geological hazards. Quantitative analysis of the spatial distribution characteristics of geological hazards and measuring its fractal relation on a national scale are significant for the geological hazards prevention or mitigation. In this contribution, firstly, three typical geological hazards, such as landslides, collapses and mudslides, were taken as research objects for fractal analysis, and a detailed hazard inventory including 109,008 landslides, 55,178 collapses, and 28,914 mudslides cases were compiled as data samples. Next, the fractal dimensions describing the spatial distribution characteristics of geological hazard densities were calculated by the invariant fractal model, and then the internal classification of five common predisposing factors (elevation, slope, aspect, NDVI, and precipitation) was applied, and the relative density of geological hazard was calculated by the ratio of "hazard ratio" and "grid ratio" on the basis of 1 km × 1 km grid cells. Finally, the variable fractal model was introduced for measuring the spatial association among three typical geological hazards and five common predisposing factors, and the obtained fractal dimensions were regarded as the quantitative measure of the effect of predisposing factors on geological hazards. The results shows that the fractal dimensions of spatial distribution of landslide, collapse and mudslide densities are 1.3042, 1.5185 and 1.5897, respectively. Moreover, the relative densities of geological hazards also follows the fractal features with hazard-related predisposing factors, the elevation factor has the greatest impact on the landslide, collapse, and mudslide hazard, while other predisposing factors have different effects on different types of geological hazards.

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Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

Abstract: 2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (

Cited by 438 publications

References 74 publications

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

Fractal-Based Spatial Distribution Analysis of Geological Hazards and Measurement of Spatial Association With Hazard-Related Predisposing Factors

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