Method for assessing hazard on flood sensitivity based on ensemble learning

A method for assessing a hazard on flood sensitivity based on an ensemble learning, wherein the method comprises following steps:

• Step one, collecting and sorting initial data of sample points:
• drawing a position map of a flood in a basin by using literature materials and surveying on site, and creating a spatial database related to the flood;
• selecting regulating factors through data obtained from the literature materials and the survey on site;
• selecting a plurality of flood regulating factors to conduct a sensitivity analysis, and establishing a spatial database of the factors;
• Step two, cleaning and standardizing the collected initial data, assigning the initial data to each evaluation unit, converting the initial data into a raster data storage format, and performing a projection conversion and a resampling operation on all of the data;
• acquiring, for each research region, historical flow data from a corresponding hydrological station, retrieving a date for each year, on which a flood flow crest value occurs, and selecting MODIS images corresponding to the date to reflect an inundation status during the flood;
• superimposing inundation ranges reflected by the plurality of images corresponding to the flow crest value to generate a combined maximum inundation range map as an inundation range map, namely a maximum inundation range, corresponding to the flow crest value;
• randomly selecting N flood inundation sample points within the maximum inundation range, and randomly selecting N non-flood inundation sample points within a non-maximum flood inundation range to form sample points with a total number of 2N together;
• dividing the sample points into a training set and a testing set, wherein 70% of the sample points are taken as the training set, and 30% of the sample points are taken as the testing set;
• Step three, calculating Laplace scores to determine eventual feature subsets:
• scoring, by using the Laplace scores, features of the samples in the training set in Step two to obtain a score for each feature, and eventually taking k features with highest scores as selected feature subsets;
• extracting the feature subsets from the sample points with the total number of 2N in Step two to form a new training set and a new testing set;
• Step four, training, by using the new training set in Step three, a LightGBM model of the ensemble learning, and obtaining accuracy rates of the LightGBM model of the ensemble learning for the new training set and the new testing set;
• and
• Step five, calculating, by using the trained model, for the whole basin to obtain a probability value for the hazard on the flood sensitivity in the whole basin;
• wherein the plurality of flood regulating factors in Step one include:
• atmosphere, evaporation, topography, and river networks;
• 10 indicators, namely features, for assessing the hazard on the flood sensitivity, including elevations, gradients, curvatures, TWI, SPI, distances from rivers, soil, vegetation, slope directions and rainfalls are proposable from the 4 factors;
• according to a mechanism of the flood in the basin, the factors are calculated and processed based on an ArcGIS software, and the SPI and the TWI are calculated by using following formulas:
• TWI
• =
• L
• n
• α
• /
• tan
• ​
• β
• SPI
• =
• A
• s
• tan
• β
• wherein α represents a cumulative slope water discharge through one point, As represents a specific basin area, and tan β represents a gradient angle at the point.