A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

被引：107

作者：

Pham B.T. ^{[1
,2
]}

Tien Bui D. ^{[3
]}

Dholakia M.B. ^{[4
]}

Prakash I. ^{[5
]}

Pham H.V. ^{[6
]}

机构：

[1] Department of Civil Engineering, Gujarat Technological University, Nr.Visat Three Roads, Visat - Gandhinagar Highway, Chandkheda, Ahmadabad, 382424, Gujarat

[2] Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi

[3] Geographic Information System Group, Department of Business Administration and Computer Science, Telemark University College, Hallvard Eikas Plass 1, Bø i Telemark

[4] Department of Civil Engineering, LDCE, Gujarat Technological University, Ahmadabad, 380015, Gujarat

[5] Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar

[6] Vietnam Institute of Geosciences and Mineral Resources, Thanh Xuan, Hanoi

来源：

Geotechnical and Geological Engineering | 2016年 / 34卷 / 6期

关键词：

Landslides; GIS; Least square support vector machine; Multiclass alternating decision tree; Vietnam; Yen Bai;

D O I：

10.1007/s10706-016-9990-0

中图分类号：

学科分类号：

摘要：

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards. © 2016, Springer International Publishing Switzerland.

引用

页码：1807 / 1824

页数：17

共 76 条

[1]

Ahlheim M., Landslides in mountainous regions of Northern Vietnam: causes, protection strategies and the assessment of economic losses. University of Hohenheim, Germany, pp. 1-19, (2008)

[2]

Akgun A., A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey, Landslides, 9, pp. 93-106, (2012)

[3]

Aksoy B., Ercanoglu M., Landslide identification and classification by object-based image analysis and fuzzy logic: an example from the Azdavay region (Kastamonu, Turkey), Comput Geosci, 38, pp. 87-98, (2012)

[4]

Aleotti P., Chowdhury R., Landslide hazard assessment: summary review and new perspectives, Bull Eng Geol Environ, 58, pp. 21-44, (1999)

[5]

Alizadehsani R., Et al., A data mining approach for diagnosis of coronary artery disease, Comput Methods Programs Biomed, 111, pp. 52-61, (2013)

[6]

Alkhasawneh M.S., Ngah U.K., Tay L.T., Isa N.A.M., Al-Batah M.S., Modeling and testing landslide hazard using decision tree, J Appl Math, 2014, pp. 1-9, (2014)

[7]

Althuwaynee O.F., Pradhan B., Park H.-J., Lee J.H., A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping, CATENA, 114, pp. 21-36, (2014)

[8]

Ayalew L., Yamagishi H., The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65, pp. 15-31, (2005)

[9]

Baylar A., Hanbay D., Batan M., Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs, Expert Syst Appl, 36, pp. 8368-8374, (2009)

[10]

Begueria S., Validation and evaluation of predictive models in hazard assessment and risk management, Nat Hazards, 37, pp. 315-329, (2006)

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