基于混合细分算法的水湖自动监控报警方法
Ziming Cai1, Liang Sun2, Baosheng An3
1School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
一个新的混合细分算法通过监测河流水位,自动检测山区的湖灾难. 这种无人驾驶系统提供了高精度和适应性,用于预防灾害.
科学领域:
- 地质科学 地质科学
- 环境监测 环境监测
- 遥感 遥感 遥感 遥感
背景情况:
- 山区面临着大湖灾难造成的重大风险.
- 这些灾难往往是由自然事件引发的,比如泥石流堵塞河流.
- 有效的监测系统对于早期检测和缓解至关重要.
研究的目的:
- 提出一个自动监测和警报方法,用于水湖事件.
- 开发一个利用混合细分算法的系统,以提高准确性.
- 在现实世界的河流流域中实施和测试该系统.
主要方法:
- 开发了一种混合细分算法,将k-means集群和区域增长结合起来.
- 该k-means算法在RGB色彩空间中对场景进行细分.
- 在绿色通道上生长的地区确定了水位分析的河流目标.
主要成果:
- 该系统的准确率为89.29%,失误率为11.76%.
- 性能显示出与传统的区域生长算法相比显著改善.
- 该系统成功监测了Yarlung Tsangpo河流域的水位变化.
结论:
- 拟议的混合细分算法为无人水湖监测提供了准确和适应性的解决方案.
- 这种方法增强了对潜在的水湖灾难的早期预警能力.
- 该系统在具有挑战性的山区环境中证明了其实际适用性.
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