Debris Flow Infrasound Recognition Method Based on Improved LeNet-5 Network

Xiaopeng Leng, Liangyu Feng (), Ou Ou, Xuelei Du, Dunlong Liu and Xin Tang
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Xiaopeng Leng: College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China
Liangyu Feng: College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China
Ou Ou: College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China
Xuelei Du: College of Computer Science and Cyber Security (Oxford Brookes College), Chengdu University of Technology, Chengdu 610059, China
Dunlong Liu: College of Software Engineering, Chengdu University of Information and Technology, Chengdu 610225, China
Xin Tang: Chenglizhiyuan Technology (Chengdu Co., Ltd.), Chengdu 610059, China

Sustainability, 2022, vol. 14, issue 23, 1-13

Abstract: To distinguish debris flow infrasound from other infrasound sources, previous works have used one-dimensional infrasound shapes and parameters. In this study, we converted infrasound signals into two-dimensional signal time–frequency graphs and created a time–frequency graph dataset containing five common kinds of infrasound. We used deep learning to distinguish debris flow infrasound from other infrasound and improved the deep learning model to enhance the accuracy of debris flow infrasound identification. By improving the LeNet-5 network, we obtained an infrasound signal recognition method for debris flows based on deep learning. After signal preprocessing and model training, this method was able to differentiate target infrasound from environmental infrasound, and a debris flow infrasound recognition accuracy of 84.1% was achieved. The method described in this paper can effectively recognize debris flow infrasound and distinguish it from other environmental infrasound. By such means, more accurate and more timely debris flow disaster warnings may be obtained.

Keywords: debris flow; infrasound; neural network; image recognition; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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