Analysis of secondary-factor combinations of landslides using improved association rule algorithms: a case study of Kitakyushu in Japan

Jiaying Li, Wei Dong Wang, Zheng Han, Guangqi Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Landslide analysis prevents landslides from threatening resident safety and property, and the predominant method is susceptibility assessment which is cumbersome and time-consuming. The association rule algorithm (ARA) is proposed to mine the correlation between the factors and landslides simply and rapidly. The original ARA cannot reflect the scope of landslides which is non-negligible for landslide analysis and is thus improved to mine the frequent secondary-factor combinations (SFCs). Firstly, eight factors are selected using the out-of-bag error and chi-squared ((Formula presented.)) test. The accuracy of the factor selection is further verified employing landslide susceptibility assessment which is predicted using 30% of study grid data selected randomly as the training data. The improved ARA employs the area of historical landslides to mine the frequent SFCs, and the results are then verified by the frequency ratio and (Formula presented.) test. It is concluded that the frequent SFCs are: (21, 41), (21, 74), (34, 41), (34, 74), (41, 74), (21, 41, 74), and (34, 41, 74), and the area with the SFCs needs special protection. The present study provides a valuable reference for the primary prevention of landslides.

Original languageEnglish
Pages (from-to)1885-1904
Number of pages20
JournalGeomatics, Natural Hazards and Risk
Volume12
Issue number1
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

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