Flood disaster remote sensing monitoring evaluation method based on machine learning

Abstract

The invention discloses a flood disaster remote sensing monitoring evaluation method based on machine learning, and aims to perform remote sensing monitoring on a flood disaster area based on a deep learning method, and perform prediction and disaster situation evaluation on a range possibly submerged by flood by using a random forest algorithm in machine learning. And important data and theoretical support can be provided for flood control decision and disaster relief work. The method comprises the following steps: step 1, extracting a flood inundation area range; based on Sentinel-1A SAR data of microwave remote sensing, an area submerged by flood is accurately extracted; 2, estimating the water depth of the flood coverage area; estimating the water depth of the flood coverage area based on DEM product data; step 3, flood range prediction and disaster assessment; based on data of meteorological data, topographic data, drainage basin attribute characteristics and the like, the method is used for predicting a coverage range when flood occurs and evaluating risks caused by the flood.

Description

technical field [0001] The invention relates to the technical field of flood disaster monitoring and evaluation, belongs to the technical field of cartography and geographic information systems, and in particular relates to a machine learning-based flood monitoring and risk assessment method. Background technique [0002] As a kind of natural disaster, flood disaster is often unavoidable. As one of the important non-engineering measures, flood disaster monitoring is very necessary. At present, scientific and effective flood monitoring is an important basis for flood control and disaster relief. Remote sensing monitoring has gradually become the main means of modern flood disaster monitoring due to its obvious advantages such as convenient data acquisition, high image resolution, and wide data coverage. It can effectively make up for the shortcomings of traditional monitoring methods such as small coverage, time-consuming, labor-intensive, and expensive sampling surveys, an...

AI Technical Summary

Problems solved by technology

Machine learning is a general term for a class of algorithms that attempt to mine hidden laws from a large amount of historical data and use them for prediction or classification. More specifically, machine learning can be seen as looking for a function whose input is sample data , the output is the desired result, but this function is too complicated to be formally expressed

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