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Open AccessArticle

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

1
Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran
2
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia
3
Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
4
Faculty of Earth Sciences, Kharazmi University, Tehran 14911-15719, Iran
5
Division of Science Education, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si 24341, Korea
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 999; https://doi.org/10.3390/rs11090999
Received: 19 March 2019 / Revised: 24 April 2019 / Accepted: 24 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures. View Full-Text
Keywords: frequency ratio; random forest; landslide susceptibility; GIS; remote sensing frequency ratio; random forest; landslide susceptibility; GIS; remote sensing
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MDPI and ACS Style

Arabameri, A.; Pradhan, B.; Rezaei, K.; Lee, C.-W. Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs. Remote Sens. 2019, 11, 999.

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