Abstract
Abstract. Landslide is a frequently observed natural phenomenon and a geohazard with destructive effects on economies, society and the environment. Production of up-to-date landslide susceptibility (LS) maps is an essential process for landslide hazard mitigation. Obtaining up-to-date and accurate data for the production of LS maps is also important and this task can be achieved by using aerial photogrammetric techniques, which can produce geospatial data with high resolution. The produced geospatial datasets can be integrated in data-driven methods for obtaining accurate LS maps. In the present study, LS map was produced by using data-driven machine learning (ML) methods, i.e. random forest (RF). An earthquake and landslide prone area from the south-eastern part of Turkey was selected as the study area. Topographical derivatives were extracted from digital surface models (DSMs) produced by using aerial photogrammetric datasets with 30 cm ground sampling distances. The lithological parameters were employed in the study together with an accurate landslide inventory, which were also delineated by using the high-resolution DSMs and orthophotos. The relationships between the landslide occurrence and the pre-defined conditioning factors were analyzed using the frequency ratio (FR) method. The results show that the RF method exhibits high prediction performance in the study area with an area under curve (AUC) value of 0.92.
Highlights
Geological hazards are natural phenomena that may cause physical, economic and social losses; and threaten the environment and human lives
Various statistical and machine learning (ML) methods were proposed by researchers, such as analytical hierarchy process (AHP) (Pourghasemi et al, 2012), frequency ratio (FR) (Yi et al, 2019), decision trees (DT) (Wang et al, 2016), random forest (RF) (Karakas et al, 2020), logistic regression (LR) and artificial neural networks (ANN) (Sevgen et al, 2019), etc
The Landslide susceptibility (LS) map produced using high resolution digital surface models (DSMs) and orthophotos generated from aerial orthophotos by performing the RF algorithm was evaluated for a region prone to multiple hazards, i.e. earthquake and landslides
Summary
Geological hazards are natural phenomena that may cause physical, economic and social losses; and threaten the environment and human lives. There is a significant increase in the number of studies on natural hazards using various geospatial data sources and resolutions in recent years. Landslides are among the most common and destructive natural hazards in many parts of the world. The number of LS mapping studies conducted in recent years have increased in the literature. For this purpose, various statistical and machine learning (ML) methods were proposed by researchers, such as analytical hierarchy process (AHP) (Pourghasemi et al, 2012), frequency ratio (FR) (Yi et al, 2019), decision trees (DT) (Wang et al, 2016), random forest (RF) (Karakas et al, 2020), logistic regression (LR) and artificial neural networks (ANN) (Sevgen et al, 2019), etc. The main research questions in LS mapping are the generalization capability of the supervised ML methods and the availability of accurate and upto-date data for the model training in such approaches
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