TY  - CONF
ID  - scholars8136
TI  - Recent developments in machine learning applications in landslide susceptibility mapping
N2  - While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field. © 2017 Author(s).
N1  - cited By 5; Conference of 13th IMT-GT International Conference on Mathematics, Statistics and their Applications, ICMSA 2017 ; Conference Date: 4 December 2017 Through 7 December 2017; Conference Code:132050
AV  - none
VL  - 1905
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036638306&doi=10.1063%2f1.5012210&partnerID=40&md5=d5275228ac8aaf7843bb3bea467a5749
A1  - Lun, N.K.
A1  - Liew, M.S.
A1  - Matori, A.N.
A1  - Zawawi, N.A.W.A.
SN  - 0094243X
PB  - American Institute of Physics Inc.
Y1  - 2017///
ER  -