环境科学
植被(病理学)
自回归积分移动平均
边坡稳定性
湿度
气象学
遥感
时间序列
地质学
岩土工程
计算机科学
地理
医学
机器学习
病理
作者
Dunwen Liu,Haofei Chen,Yu Tang,Chao Liu,Wanmao Zhang,Chun Gong,Shulin Jiang
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2022-02-05
卷期号:22 (3): 1214-1214
被引量:9
摘要
The rapid development of highway engineering has made slope stability an important issue in infrastructure construction. To meet the needs of green vegetation growth, ecological recovery, landscape beautification and the economy, long-term monitoring research on high-slope micrometeorology has important practical significance. Because of that, we designed and created a new slope micrometeorological monitoring and predicting system (SMMPS). We innovatively upgraded the cloud platform system, by adding an ARIMA prediction system and data-fitting system. From regularly sensor-monitored slope micrometeorological factors (soil temperature and humidity, slope temperature and humidity, and slope rainfall), a data-fitting system was used to fit atmospheric data with slope micrometeorological data, the trend of which ARIMA predicted. The slope was protected in time to prevent severe weather damage to the slope vegetation on a large scale. The SMMPS, which upgrades its cloud platform, significantly reduces the cost of long-term monitoring, protects slope stability, and improves the safety of rail and road projects.
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