Rainfall-based criteria for assessing slump rate of mountainous highway slopes: A case study of slopes along Highway 18 in Alishan, Taiwan

Chang, Shun Kung; Lee, Der Her; Wu, Jian Hong; Juang, C. Hsein

doi:10.1016/j.enggeo.2011.01.001

Cited by 30 publications

(14 citation statements)

References 63 publications

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“…Previous research has indicated that there is a strong correlation between mean annual precipitation and landslide occurrences [ 61 , 62 , 63 ]. According to the existing and local observation data, mean annual precipitation is divided into seven classes based on equal interval method as follows: <360 mm/y, 360–380 mm/y, 380–400 mm/y, 400–420 mm/y, 420–440 mm/y, 440–460 mm/y, and >460 mm/y ( Figure 2 e).…”

Section: Data Usedmentioning

confidence: 99%

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Zhang¹,

Chen

Shahabi

2018

Entropy

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The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

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Section: Data Usedmentioning

confidence: 99%

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Zhang¹,

Chen

Shahabi

2018

Entropy

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“…In addition to the collapse of slopes, the geohazard in region IV.A also resulted in ground collapse caused by unreasonable drainage facilities. In these regions, slope protection and reasonable drainage facilities should be strengthened in highway construction (Chang et al 2011;Liu et al 2018). 4.…”

Section: Geohazards Regionalizationmentioning

confidence: 99%

Geohazards regionalization along highways in Shandong Province, China

Yang

Zhao

Cheng

2020

Geomatics, Natural Hazards and Risk

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The disturbance of the highway construction to the terrain and the natural environmental factors are the main causes of the geohazards along the highway. We collected geohazards along the highway in Shandong Province and put forward the comprehensive geohazard index to reflect the risk of geohazards along the highway. The index consists of an intensity index describing the development of geohazards and a sensitivity index describing the potential risk of geohazards. The article uses an incomplete additive language matrix to calculate the weight of each index and factor. Based on the Getis-Ord Gi Ã statistic tool, the spatial distribution of landslides and collapses, debris flows, ground collapses, and unstable slopes are analyzed, and the geohazard intensity index is calculated by spatial superposition. The relationship between slope gradient, rainfall, vegetation coverage, rock type and geohazards is analyzed, and the geohazard sensitivity index is calculated by spatial superposition. Based on the comprehensive geohazard index and geomorphological characteristics, Shandong Province is divided into 4 geohazard first-level regions and 11 geohazard second-level regions, providing a basis for highway planning and construction.

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“…In addition to the above traditional statistical methods, various machine learning techniques have been introduced for landslide susceptibility mapping, such as artificial neural networks [ 24 , 36 , 37 , 38 , 39 ], support vector machines [ 34 , 40 , 41 , 42 ], naïve Bayes trees [ 43 , 44 , 45 ], alternating decision trees [ 46 , 47 , 48 ], rotation forests [ 32 , 49 , 50 ], kernel logistic regression [ 51 , 52 ], adaptive neuro-fuzzy inference systems [ 34 , 53 , 54 ], logistic model trees [ 49 , 52 ], and classification and regression trees [ 55 , 56 , 57 ]. However, the best method for landslide susceptibility mapping is still under discussion [ 58 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

et al. 2019

Entropy

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Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

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Rainfall-based criteria for assessing slump rate of mountainous highway slopes: A case study of slopes along Highway 18 in Alishan, Taiwan

Cited by 30 publications

References 63 publications

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Geohazards regionalization along highways in Shandong Province, China

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

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