Abstract
Abstract. Landslide susceptibility and hazard mapping has been developed providing remarkable results through the integration of geographic information system (GIS) and remote sensing. In this regard, some approaches have considered the use of Sentinel-1 data and time-series interferometric synthetic aperture radar (InSAR) techniques, such as differential InSAR (D-InSAR) and persistent scatterers interferometric (PSI), for providing precise information about total amount and velocity of ground-surface deformations and landslides within a specific area during a specific time period which is important for disaster management’s planning process.In this paper, artificial neural network (ANN) was used as a statistical analysis method for landslide susceptibility mapping in Northwest Syria using multi-layer perceptron (MLP) neural network on a training dataset of one dependent variable (landslide or non-landslide) and nine independent variables (slope, aspect, curvature, land cover, NDVI, lithology, distance from faults, distance from road, distance from stream networks). The resulting map of landslide susceptibility was validated using area under curve (AUC) analysis using a testing dataset which showed 90.28% of AUC value. Then, landslide susceptibility map was reclassified into high-moderate-low classes and integrated with intensity map of mean velocity of ground-surface deformations during the time period form (16 October 2018) until (21 March 2019) by using a landslide hazard matrix in a GIS environment in order to get landslide hazard map of the study area for that time period. The result shows that around 44.4%, 52.9% and 2.5% of total study area was classified as a high, moderate and low hazard zone of landslide, respectively.
Highlights
Landslide hazard investigation, assessment, monitoring and mapping processes become increasingly important in most mountainous and hilly regions around the world where landslides are a major hazard and (Hammad et al, 2019)
Assessment, monitoring and mapping processes become increasingly important in most mountainous and hilly regions around the world where landslides are a major hazard and (Hammad et al, 2019). Landslide studies in those places are important to evaluate the relative contributions of all elements and factors involved in landslide process within an area and to identify the possible locations that might be affected by landslides in order to reduce risk on people in those areas and help local authorities in the process of future regional planning
Artificial neural network (ANN) analysis which can be applied to landslide susceptibility is a computational mechanism that can acquire, represent, and compute a mapping from one multivariate space of information to another, given a dataset representing that mapping (Garrett, 1994)
Summary
Assessment, monitoring and mapping processes become increasingly important in most mountainous and hilly regions around the world where landslides are a major hazard (kervyn et al, 2015) and (Hammad et al, 2019). Landslide investigation has been developed providing remarkable results, especially through the integration of optical remote sensing data and geographic information system (GIS) using different statistical analysis models in order to investigate and map susceptibility and hazard of landslides (Biswajeet, Saro, 2007). It has been shown that synthetic aperture radar (SAR) satellite data can be used as a complementary data source providing useful and precise information about ground-surface deformation and landslides (Hammad et al, 2018) In this direction, some approaches have already considered the use of time series interferometric SAR (InSAR) techniques for providing information about stability of areas suffering from ground-surface deformations and landslides by analysing velocity and amount of these ground-surface deformations (Hammad et al, 2018). This paper attempts to integrate free interferometric data with help of GIS in order to investigate and determine locations of possible future landslides in northwest Syria and to assess landslide hazard during the study period
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