Landslide hazards mapping using uncertain Naïve Bayesian classification method

Mao, Yimin; Zhang, Maosheng; Wang, Gen-long; Sun, Peide

doi:10.1007/s11771-015-2891-1

Cited by 22 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e traditional Euclidean distance formula can process only those nodes whose values are continuous and discrete [50]. In the rainfall-induced regional landslide hazard assessment model, the data types of the node's attributes (landslide conditioning factors) include discrete (slope aspect), continuous (slope height), and uncertain (rainfall) values [37].…”

Section: Ca-aqd Algorithmmentioning

confidence: 99%

“…On account of initial parameter thresholds dependence, k-means and FCM algorithms were not taken into consideration. Furthermore, uncertain decision tree (found in literature DTU) [33] and uncertain naïve Bayesian (found in literature NBU) [37] classification algorithms were applied against the proposed model in quantifying the value of rainfall and attaining better prediction accuracy. At the same time, the state-of-the-art benchmark models such as SVM, ANN, and RF [5] can also be chosen for checking the ability of the proposed model.…”

Section: Models Comparisonmentioning

confidence: 99%

“…Moreover, to compare the performance of landslides prediction models between classification (supervised) and clustering analysis algorithms (unsupervised), accuracy measure was used to validate the DTU, NBU, SVM, ANN, RF, and CA-AQD algorithms, as shown in Figure 11. Landslide susceptibility map based on DTU and NBU algorithms can be found in the literature [33,37], in which the 506 points (landslides) were used to develop and evaluate DTU and NBU prediction model. us, we divided the 506 points from the literature into training and test data sets.…”

Section: Model Validation As Most Landslides Occurred Frommentioning

confidence: 99%

“…Prediction models of landslides based on classification algorithms in data mining can overcome such difficulties. Specifically, being interested in coming up with ideal learning methods to determine the nonlinear relationship among landslides and the environmental factors [26], many researchers have successfully adopted them, for example, support vector machine [27][28][29], decision tree [30][31][32][33][34][35], naïve Bayesian [36,37], artificial neural networks [38][39][40][41], random forest models [42,43], and others, to construct landslide susceptibility map. ese models, however, depend on a big training data set to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Ca-aqd Algorithmmentioning

confidence: 99%

Section: Models Comparisonmentioning

confidence: 99%

Section: Model Validation As Most Landslides Occurred Frommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…The second most popular type of the model involves machine learning (15.8%), considering the nonlinear relationship between environmental factors and landslides. These models include support vectors (Kumar et al 2017;Bui et al 2018;Huang and Zhao 2018), naïve Bayesian (Mao et al 2015;Cheng and Hoang 2016;Jaafari et al 2017;Al et al 2017), decision trees (Althuwaynee et al 2014;Wu et al 2014;Mao et al 2017;Khosravi et al 2018;Park et al 2018), and neural networks (8.3%) (Gorsevski et al 2016;Saro et al 2016;Huang and Xiang 2018;Ortiz and Martinez-Grana 2018). The models depend on a large amount of landslide inventory information to improve prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Wang³

et al. 2020

Bull Eng Geol Environ

View full text Add to dashboard Cite

Because of the strong dependence on the values for the input parameters and the cluster shape, as well as the difficulties in quantifying the precipitation in constructing landslide susceptibility maps by employing existing clustering algorithms, we propose a novel method based on an Ordering Points to Identify the Clustering Structure (OPTICS) algorithm using the Hausdorff distance (OA-HD). The OA-HD algorithm distributes mapping units into many subclasses with similar characteristic values for topography and geology. To obtain more optimal subclasses, the HD was adopted to quantify precipitation. The Kmedoids algorithm grouped these subclasses into five susceptibility levels according to the values of landslide density in each subclass. Applying the innovative integrated algorithms to the study area significantly improves the landslide susceptibility assessment, especially in a large study area. The method suggests new insights for better assessing landslide susceptibility in a large study area.

show abstract

Construction and Application of Fuzzy Ontology

Liu

Zheng

2017

Advances in Computer Science and Ubiquitous Computing

View full text Add to dashboard Cite

Landslide hazards mapping using uncertain Naïve Bayesian classification method

Cited by 22 publications

References 19 publications

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

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

A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Construction and Application of Fuzzy Ontology

Contact Info

Product

Resources

About