An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand

Teerarungsigul, Suree; Torizin, Jewgenij; Fuchs, Michael; Kühn, Friedrich; Chonglakmani, Chongpan

doi:10.1007/s10346-015-0659-1

Cited by 29 publications

(24 citation statements)

References 28 publications

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“…However, it is worth noting that some research has pointed out a correlation of a certain factor class with landslide distribution is not necessarily linked to the initiation of a landslide, and it may coincide with classes of other causative factors, which are possibly the major causes for the intrinsic driven force of a landslide [ 23 , 24 ]. For example, in regions where weak strata of sericite phyllite and schist belonging to epimetamorphic rock predominate, the landslides are concentrated under conditions of relative lower slope angles.…”

Section: Resultsmentioning

confidence: 99%

“…As mentioned above, an assumption of conditional independence of factors should be predetermined before performing landslide susceptibility analysis. Some researchers have indicated that an increasing number of predictive variables raise the possibility of conditional dependence and result in unreliable outcomes [ 23 ]. For example, geomorphological attributes of the curvature, was strongly related with local slope angle and relative position of the slope.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, a widely-used method for the assessment of conditional independency for pairs of causative factors in this research is chi-square statistic test, which is related to the variation between expected and observed frequency in landslides occurrence with respect to a certain factor. Detailed description about the testing method can be seen in some previous published literature [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

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Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

et al. 2021

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The aim of this study is to provide a landslide susceptibility map of the Xunyang District of a mountainous terrain, at the southern part of the Qin-Ba Mountain Region, which has been highly exposed to widely distributed shallow landslides over the past few decades. The Weight of Evidence (WoE) method was adopted in this research considering both the presence of a certain landslide causative factor class and the absence of remaining classes, which was used for determining a clearly spatial correlation between a landslide occurrence and the causative factors. Intrinsic factors, including geomorphological factors, geological factors, and river flow networks, and external factors of anthropogenic engineering activities in terms of density of road network were all considered and involved in the Geological Information System (GIS) environment for reconstructing the thematic layers of factor dataset. Significant assumptions prior to the analysis were emphasized to ensure conditional independence between each pair of factors for this bivariate statistical approach. In addition, a detailed landslide inventory map was constructed through field investigation and a remote sensing interpretation process at a scale of 1:50000. The thematic layers and landslide map were overlapped to obtain a spatial statistical relationship by using the frequency ratio method. At last, the validation process for the derived susceptibility map was conducted by applying the ROC curve, indicating that more than 90% of the landslides were in categories of high and moderate susceptibility zones. The causative factor classes, including the slope angles ranging from 20 to 40°, strong weathered and fractured strata, and road network density were identified to considerably influence the landslide distribution in the study area. The results have proven to be significantly meaningful for landslide hazard risk mitigation and land use management for the local authorities responsible for these fields.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

et al. 2021

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“…e second model is to establish the function relationship or expression between the landslide and factors by selecting an appropriate mathematical means, so as to conduct landslide susceptibility mapping, for example, frequency ratio [17][18][19], weights-of-evidence [20][21][22][23], certainty factors [24][25][26], and logistic regression [27][28][29]. e occurrence of landslides is complicated nonlinear and affected by conditioning factors, including geomorphological, geological, hydrological, surface cover index, geophysical, and meteorological factors [30,31].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

Zhao

2021

Mathematical Problems in Engineering

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The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

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“…Given that appropriate soils and rocks engineering data, slope geometry, discontinuity features, and hydrological factors are required to compute the resisting and driving forces association, deterministic models have been limited to small study areas [9,10]. Statistical models, such as linear and logistic regression [11][12][13], bivariate statistical models [14][15][16], frequency ratio [17][18][19][20][21], and weight of evidence models [22][23][24], have been applied widely to the field of constructing assessment models for landslide susceptibility. ese models, however, cannot easily determine the relationship between significant landslide-influencing factors and complicated landslide systems [25].…”

Section: Introductionmentioning

confidence: 99%

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

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This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

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An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand

Cited by 29 publications

References 28 publications

Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

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