Review on landslide susceptibility mapping using support vector machines

By Yu Huang, Lu Zhao. CATENA, 2018

645 citations recorded by the source corpus.

Abstract Landslides are natural phenomena that can cause great loss of life and damage to property. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. A support vector machine (SVM) is a machine learning algorithm that uses a small number of samples for prediction and has been widely used in recent years. This paper presents a review of landslide susceptibility mapping using SVM. It presents the basic concept of SVM and its application in landslide susceptibility assessment and mapping. Then it compares the SVM method with four other methods (analytic hierarchy process, logistic regression, artificial neural networks and random forests) used in landslide susceptibility mapping. The application of SVM in landslide susceptibility assessment and mapping is discussed and suggestions for future research are presented. Compared with some of the methods commonly used in landslide susceptibility assessment and mapping, SVM has its strengths and weaknesses owing to its unique theoretical basis. The combination of SVM and other techniques may yield better performance in landslide susceptibility assessment and mapping. A high-quality informative database is essential and classification of landslide types prior to landslide susceptibility assessment is important to help improve model performance.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/37993d9cedee36419b8cce815e1db5cad9faf5b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Ataollah Shirzadi
2. Himan Shahabi
3. Kamran Chapi
4. N. Al‐Ansari
5. Jie Dou

In Management, Monitoring, Policy and Law

1. Ataollah Shirzadi
2. Kamran Chapi
3. Jie Dou
4. Himan Shahabi

In Mechanical Engineering

1. Kamran Chapi
2. Ataollah Shirzadi
3. Jie Dou
4. Himan Shahabi

Citations in the corpus

• Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, methodology): “Several methods have been used to determine the importance of different factors for landslide occurrence, notably fuzzy-rough theory [95], relief algorithm [96], information gain, and information gain ratio [97].”