Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Li, Yang; Chen, Wei

doi:10.3390/w12010113

Cited by 83 publications

(33 citation statements)

References 123 publications

(184 reference statements)

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“…LR is able to investigate a series of glitches where the results are impacted by one or more factors. The factors influencing the results are referred to as independent variables, which can be discrete or continuous, or a combination of discreet and continuous [48]. Logistic regression allows the forecasting of discrete outcomes, such as group membership, from a set of variables that may be continuous, discrete, or a mixture of any of these types.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

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Climate change has increased the probability of the occurrence of catastrophes like wildfires, floods, and storms across the globe in recent years. Weather conditions continue to grow more extreme, and wildfires are occurring quite frequently and are spreading with greater intensity. Wildfires ravage forest areas, as recently seen in the Amazon, the United States, and more recently in Australia. The availability of remotely sensed data has vastly improved, and enables us to precisely locate wildfires for monitoring purposes. Wildfire inventory data was created by integrating the polygons collected through field surveys using global positioning systems (GPS) and the data collected from the moderate resolution imaging spectrometer (MODIS) thermal anomalies product between 2012 and 2017 for the study area. The inventory data, along with sixteen conditioning factors selected for the study area, was used to appraise the potential of various machine learning (ML) methods for wildfire susceptibility mapping in Amol County. The ML methods chosen for this study are artificial neural network (ANN), dmine regression (DR), DM neural, least angle regression (LARS), multi-layer perceptron (MLP), random forest (RF), radial basis function (RBF), self-organizing maps (SOM), support vector machine (SVM), and decision tree (DT), along with the statistical approach of logistic regression (LR), which is very apt for wildfire susceptibility studies. The wildfire inventory data was categorized as three-fold, with 66% being used for training the models and 33% being used for accuracy assessment within three-fold cross-validation (CV). Receiver operating characteristics (ROC) was used to assess the accuracy of the ML approaches. RF had the highest accuracy of 88%, followed by SVM with an accuracy of almost 79%, and LR had the lowest accuracy of 65%. This shows that RF is better suited for wildfire susceptibility assessments in our case study area.

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

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“…The high spring potential class covers 7.01% of the area, whereas low and very low spring potential classes cover 68.47% of the area (Table 4). Figure 8 shows the spring potential map constructed by the ADTree model based on the natural break method [77,78]. Compared to the previous methods, the ADTree method provides a rather different spatial distribution.…”

Section: Correlation Analysis Between Springs and Explanatory Factorsmentioning

confidence: 99%

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

Chen

Tsangaratos

et al. 2020

Applied Sciences

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This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

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“…where α is the area drained per unit contour length at a point, and β is the slope [14,64]. Plan curvature illustrated the curvature degree of a contour line that forms the intersection of the horizontal plane and the ground [65].…”

Section: Preparation Of Training and Validation Datasetsmentioning

confidence: 99%

“…Statistical approaches are quantitative methods which can use different functional relationship. They can be subdivided into: (1) physically-based methods [10,11]; and (2) traditional statistical methods, such as the frequency ratio [12,13], evidential belief function [14,15], weight of evidence [16,17], discriminant analysis [18][19][20][21], and logistic regression [22,23], (3) advanced data mining technologies, such as artificial neural networks [24,25], support vector machines [26][27][28], adaptive neuro-fuzzy inference systems [29,30], alternating decision trees [31], and functional trees [32,33]. These methods need landslide inventories expressed as landslide density maps to produce functional relationships with causative factors [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Wang

Chen

2020

IJGI

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The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area.

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Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

Cited by 83 publications

References 123 publications

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

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