Abstract
Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.
Highlights
The Earth’s surface is dynamic and landslides are part of the dynamic processes, they are sometimes triggered by human intervention, especially for infrastructure construction purposes
Several auto-production methods [1,2,3] for landslide susceptibility maps have been applied in the literature and their performances have generally been compared by employing landslide inventories that are prepared manually
Aerial photogrammetric images acquired in two different years, 2012 and 2018, using a large-format aerial camera, were obtained acquired in two different years, 2012 and 2018, using a large-format aerial camera, were obtained from from the General Directorate of Mapping (GDM) in Turkey together with image orientation and the General
Summary
The Earth’s surface is dynamic and landslides are part of the dynamic processes, they are sometimes triggered by human intervention, especially for infrastructure construction purposes.Constructing a dam is an important interference to the topography and other nature characteristics of a region. The Earth’s surface is dynamic and landslides are part of the dynamic processes, they are sometimes triggered by human intervention, especially for infrastructure construction purposes. Proper site selection for such an expensive investment is crucial for its construction costs and life time. Landslides can be triggered by dams, and in return, they may affect the life time of dams. Producing accurate landslide inventories and reliable susceptibility maps are needed prior to site selection. During the last three decades, thousands of landslide susceptibility maps have been produced and published in international literature. Several auto-production methods [1,2,3] for landslide susceptibility maps have been applied in the literature and their performances have generally been compared by employing landslide inventories that are prepared manually. The main purpose of a Sensors 2019, 19, 3940; doi:10.3390/s19183940 www.mdpi.com/journal/sensors
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