A Generalized Ensemble Machine Learning Approach for Landslide Susceptibility Modeling-Reference-Cited by-同舟云学术

A Generalized Ensemble Machine Learning Approach for Landslide Susceptibility Modeling

Published:2019-09-25 Issue: Volume: Page:71-93
ISSN:2194-5357
Container-title:Data Management, Analytics and Innovation
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Author:

Bandara Akila,Hettiarachchi Yashodha,Hettiarachchi Kusal,Munasinghe Sidath,Wijesinghe Ishara,Thayasivam Uthayasanker

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-9364-8_6

Reference28 articles.

1. Karunaratne, M. (2017). Sri Lanka floods and landslides (p. 2017). Colombo: IOM Sri Lanka.

2. U. W. L. Chandradasa, Mallawatantri, A., & Wijethunga, R. (2009). Sri Lanka national report on disaster risk, poverty and human development relationship. Ministry of Disaster Management and Human Rights, Sri Lanka.

3. Subhashini, L. D. C. S., & Premaratne, H. L. (2013). Landslide prediction using artificial neural networks. In ICSBE-2012: International Conference on Sustainable Built Environment, Kandy, Sri Lanka.

4. Pham, B. T., Bui, D. T., Pourghasemi, H. R., Indra, P., & Dholakia, M. B. (2015). Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes.

5. Pradhan, B., & Lee, S. (2009). Landslide risk analysis using artificial neural network model focusing on different training sites. International Journal Physics Sciences, 3(11), 1–15.

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