Snowfall identification method based on microwave attenuation signal fusion kernel extreme learning machine

Abstract

The invention discloses a snowfall identification method based on a microwave attenuation signal fusion kernel extreme learning machine, which comprises the following steps: (1) calculating the intensity of an original microwave attenuation signal, preprocessing the signal, and obtaining snowfall attenuation signal data through a self-adaptive integrated empirical mode decomposition method; (2) setting output data training information of the kernel extreme learning machine model; (3) constructing a kernel extreme learning machine model; (4) optimizing parameters of the kernel extreme learning machine through a differential evolution algorithm to obtain an optimal kernel extreme learning machine model; and (5) outputting corresponding information through the input data of the test set to obtain snowfall intensity information corresponding to the data of the test set. The characteristics of wide coverage range, good inversion effect and the like of a wireless microwave communication link are utilized, the improved extreme learning machine algorithm is adopted, the training result precision is high, the snowfall monitoring means are improved, large-range monitoring of the snowfall intensity is achieved, and the snowfall intensity can be accurately and efficiently recognized.

Description

technical field [0001] The invention relates to a snowfall identification method based on a microwave attenuation signal fusion nuclear extreme learning machine, and belongs to the technical field of meteorological factor monitoring. Background technique [0002] Continuous high-intensity snowfall will cause snow disasters, avalanches and other hazards, posing a great threat to agriculture, road traffic and even human life. Accurate and timely identification of snowfall intensity, snowfall duration and spatial distribution plays an important role in agricultural production, transportation, and disaster prevention and mitigation. In the existing research, snowfall observation mainly includes ground observation and space-based observation, usually using snow gauge, radar, automatic snow depth observation instrument, ultrasonic snow depth instrument, satellite remote sensing, etc. to observe snowfall. The snow gauge can directly measure the snow water equivalent, and the resul...

AI Technical Summary

Problems solved by technology

However, snowfall data in my country is relatively scarce, and traditional snowfall monitoring methods often have limitations. There are a large number of mixed signals in wireless microwave attenuation signals. The signal attenuation caused by snowfall is extracted from the total attenuation, and the signal attenuation caused by snowfall is restored. It must be difficult. There are few studies on the monitoring of snowfall depth and snowfall intensity using wireless microwave links, and the technical means are not mature enough.

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