Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil

Abstract

Abstract. This study presents a methodology for susceptibility mapping of shallow landslides just from data and software from the public domain. The study was conducted in a mountainous region located on the southeastern Brazilian coast, in the state of São Paulo. The proposal is that the methodology can be replicated in a practical and reliable way in several other municipalities that do not have such mappings and that often suffer from landslide-related disasters. The susceptibility mapping was generated based on the following maps: geological, soils, slope, horizontal and vertical curvatures, and land use. The thematic classes of these maps were weighted according to technical and scientific criteria related to the triggering of landslides, and were crossed by the fuzzy gamma technique. The mapping was compared with the risk sector survey made by the Brazilian Geological Survey (CPRM), which is the official database used by municipalities and civil defense in risk management. The results showed positive correlations, so that the critical risk sectors had higher proportions for the more susceptible classes. To compare the approach with other studies using landslide-scar maps, correlated indices were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas.

Future scenarios (2021-2050) of extreme precipitation events that trigger landslides – a case study of the Paraitinga River watershed, SP, Brazil

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A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

“…e risk of landslides [62] is higher in the coastal area [63] of South, Southeast [64][65][66][67], and Northeast Brazil and of soil erosion [68] elsewhere in the country. Higher F100D3 (5%) along the coastal region is located between Paraná and Espírito Santo from November to February, while within Brazil from December to February in North and Southeast Brazil.…”

The study of artificial intelligence for predicting land use changes in an arid ecosystem

“…Another reason for the lack of studies in this area is the absence of simulation and analytical methods that apply to complex and dynamic ecosystems. Yet, such predictions of LUCC are still valuable for guiding land use planning and management (Han et al, 2015) along with risk assessment and control (Camarinha et al, 2013).…”