Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility

Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.

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Comparison of rotation forest based intelligence models for landslide susceptibility modeling

10.21203/rs.3.rs-346721/v1 ◽

2021 ◽

Author(s):

Xia Zhao ◽

Wei Chen ◽

Tao Li ◽

Faming Huang ◽

Chaohong Peng ◽

...

Keyword(s):

Decision Tree ◽

Landslide Susceptibility ◽

Spatial Prediction ◽

Validation Dataset ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Rotation Forest ◽

Conditioning Factors ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

Abstract The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

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Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Geosciences ◽

10.3390/geosciences10110430 ◽

2020 ◽

Vol 10 (11) ◽

pp. 430 ◽

Cited By ~ 1

Author(s):

Sangey Pasang ◽

Petr Kubíček

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Area Under The Curve ◽

Information Value ◽

Weight Of Evidence ◽

Regression Methods ◽

Susceptibility Maps ◽

Road Corridors ◽

Landslide Susceptibility Maps ◽

Control Samples

In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.

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Improving Voting Feature Intervals for Spatial Prediction of Landslides

Mathematical Problems in Engineering ◽

10.1155/2020/4310791 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Binh Thai Pham ◽

Tran Van Phong ◽

Mohammadtaghi Avand ◽

Nadhir Al-Ansari ◽

Sushant K. Singh ◽

...

Keyword(s):

Landslide Susceptibility ◽

Model Performance ◽

Spatial Prediction ◽

Susceptibility Assessment ◽

Improve Performance ◽

Landslide Susceptibility Assessment ◽

Ensemble Techniques ◽

Susceptibility Maps ◽

Quantitative Validation ◽

Two Hybrid

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

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A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

Geosciences Journal ◽

10.1007/s12303-015-0026-1 ◽

2015 ◽

Vol 20 (1) ◽

pp. 117-136 ◽

Cited By ~ 79

Author(s):

Liang-Jie Wang ◽

Min Guo ◽

Kazuhide Sawada ◽

Jie Lin ◽

Jinchi Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Decision Tree ◽

Comparative Study ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Weights Of Evidence ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

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Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Sensors ◽

10.3390/s20061723 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1723 ◽

Cited By ~ 9

Author(s):

Mohammad Mehrabi ◽

Biswajeet Pradhan ◽

Hossein Moayedi ◽

Abdullah Alamri

Keyword(s):

Fuzzy Inference System ◽

Landslide Susceptibility ◽

Fuzzy Inference ◽

State Of The Art ◽

Spatial Prediction ◽

Inference System ◽

Conditioning Factors ◽

Susceptibility Maps ◽

Neuro Fuzzy ◽

Landslide Susceptibility Maps

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

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Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120696 ◽

2020 ◽

Vol 9 (12) ◽

pp. 696

Author(s):

Wei Chen ◽

Zenghui Sun ◽

Xia Zhao ◽

Xinxiang Lei ◽

Ataollah Shirzadi ◽

...

Keyword(s):

Landslide Susceptibility ◽

Vegetation Index ◽

Slope Angle ◽

Spatial Prediction ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Bivariate Model ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

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Stochastic procedure to extract and to integrate landslide susceptibility maps: an example of mountainous watershed in Taiwan

Natural Hazards and Earth System Science ◽

10.5194/nhess-6-803-2006 ◽

2006 ◽

Vol 6 (5) ◽

pp. 803-815 ◽

Cited By ~ 34

Author(s):

◽

Keyword(s):

Landslide Susceptibility ◽

Full Range ◽

Model Performance ◽

Susceptibility Map ◽

Risk Area ◽

High Risk Area ◽

Susceptibility Maps ◽

Mountainous Watershed ◽

Generalized Likelihood Uncertainty Estimation ◽

Landslide Susceptibility Maps

Abstract. The Generalized Likelihood Uncertainty Estimation (GLUE) is here incorporated into a deterministic landslide model (SHALSTAB) to generate 4000 landslide susceptibility maps which enclose various combinations of full range parameters. Furthermore, an improved index is adopted into GLUE as a criterion to measure model performance, and through that, 200 maps holding top 5% performance are retrieved. Proper ranges for parameters are obtained through GLUE yet they only perform well if combined appropriately. The 200 better maps are overlapped to construct an integrated landslide susceptibility map. Instead of giving a single parameter set or a single susceptibility map, the merit of extracting and integrating procedure is to envelope uncertainties inherited in model structure and input parameters. Bias due to subjective parameter input is potentially reduced. The entire procedure is applied to the Chi-Jia-Wan, a mountainous watershed in Taiwan. The integrated map shows high-risk area (>50% predicted landslide probability) only occupies 16.4% of the entire watershed while able to correctly identify 60% of the actual landslides. For areas above 2100 m height the map is even more successful (projects 77 of the 98 actual landslides). Interactions among parameters are discussed to highlight the unsolvable equifinality problem and improperness of presenting a single model result.

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