Landslide sensitivity evaluation method based on machine learning
A machine learning and sensitivity technology, applied in instrumentation, climate sustainability, design optimization/simulation, etc., can solve problems such as unpredictability and statistical modeling, and achieve the effect of improving reliability
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[0026] The following is further described in detail by specific embodiments:
[0027] In the present invention, S1 represents step 1, S101 represents step 01 in S1, S102 represents step 02 in S1, and so on.
[0028] The specific implementation process is as follows:
[0029] Let the study area be the M area.
[0030] S1: Initial data collection of landslides in the study area:
[0031] S101: Landslide Inventory Data Acquisition: Obtain landslide catalogues in the study area through aerial orthophotogrammetry and field surveys. 260 shallow landslides have occurred in Area M, so these landslide data are collected. Since the landslides investigated are polygons, only one highest point is selected, that is, the point with the highest altitude in the landslide area. This is done to be able to run different models that require points as input data.
[0032] S102: Selection of landslide-inducing factors: 13 predictors were selected based on the most representative local morpholog...
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