Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event
| Autor: | Qing Zhou, Yingying Tian, Duo Wang, Chong Xu, Haoyuan Hong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
lcsh:GE1-350
010504 meteorology & atmospheric sciences Artificial neural network susceptibility assessment Event (relativity) lcsh:Risk in industry. Risk management 0211 other engineering and technologies Landslide 02 engineering and technology Landslide susceptibility 01 natural sciences lcsh:TD1-1066 lcsh:HD61 seismic factors seismic landslides General Earth and Planetary Sciences lcsh:Environmental technology. Sanitary engineering minxian earthquake Geology Seismology artificial neural network lcsh:Environmental sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Geomatics, Natural Hazards & Risk, Vol 10, Iss 1, Pp 1-25 (2019) |
| ISSN: | 1947-5713 1947-5705 |
| Popis: | A landslide susceptibility map, which describes the quantitative relationship between known landslides and control factors, is essential to link the theoretical prediction with practical disaster reduction measures. In this work, the artificial neural network (ANN) model, a promising tool for mapping landslide susceptibility, was adopted to evaluate the coseismic landslide susceptibility affected by the 2013 Minxian, Gansu, China, Mw5.9 earthquake. The evaluation was based on the landslide inventory of this event containing 6479 landslides, and the terrain, geological and seismic factors from database available. During the analyses, two ANN models were applied: considering the entire factors aforementioned (CS model) and excluding seismic factors above (ES model). The success and predictive rates of ANN models and the cumulative percentage curves of susceptibility maps obtained from the models all indicate that the CS model has a relatively better performance than the ES model. However, the comparison of overlapping susceptibility areas suggests that 52.8% of the very high susceptibility areas derived from the CS model coincide with the ES model; and for the very low susceptibility areas, this proportion is 73.55%. Thus, it can be concluded that the assessment based on existing earthquake-induced landslides and the ES model could provide better background information for seismic landslide susceptibility mapping and disaster prevention. |
| Databáze: | OpenAIRE |
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