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根据监视音频和深度学习来估计降雨强度
Meizhen Wang1,2,3, Mingzheng Chen1,2,3, Ziran Wang1,2,3,4
1Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China.
Environmental science and ecotechnology
|August 20, 2024
概括
监视音频现在可以估计降雨强度,为水文传感提供了一种新方法. 本研究介绍了一个数据集和深度学习模型,为改进城市降雨量监测铺平了道路.
科学领域:
- 水文学的水文学
- 环境传感器环境传感器
- 机器学习 机器学习
背景情况:
- 高分辨率的降雨数据对于城市水文建模至关重要.
- 监控摄像机可以通过音频和视频监控降雨情况.
- 现有的基于音频的方法往往缺乏降雨强度估计能力.
研究的目的:
- 介绍监控音频降雨强度数据集 (SARID).
- 开发和验证一个深度学习模型,从监视音频中估计降雨强度.
- 探索基于音频的传感对于城市降雨观测的潜力.
主要方法:
- 从六个现实世界降雨事件创建了SARID,对音频进行细分,并对强度和环境数据进行注释.
- 开发了一个使用Mel-Frequency Cepstral Coefficients (MFCC) 和变压器架构的深度学习基线.
- 验证模型与地面真相数据进行性能评估.
主要成果:
- 开发的深度学习模型实现了0.88mmh-1.1的根平均绝对误差.
- 获得0.765的相关系数,证明了模型的准确性.
- 该SARID数据集包含12,066个有详细注释的音频段.
结论:
- 基于监控音频的模型显示了实际和有效的降雨观测的巨大潜力.
- 这项工作启动了使用新数据源进行降雨强度估计的新方法.
- 这些发现有助于高分辨率的水文传感,城市传感和应急响应系统.
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