Cargando…
Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System
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 impor...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838333/ https://www.ncbi.nlm.nih.gov/pubmed/35161957 http://dx.doi.org/10.3390/s22031214 |
_version_ | 1784650101718777856 |
---|---|
author | Liu, Dunwen Chen, Haofei Tang, Yu Liu, Chao Cao, Min Gong, Chun Jiang, Shulin |
author_facet | Liu, Dunwen Chen, Haofei Tang, Yu Liu, Chao Cao, Min Gong, Chun Jiang, Shulin |
author_sort | Liu, Dunwen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8838333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88383332022-02-13 Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System Liu, Dunwen Chen, Haofei Tang, Yu Liu, Chao Cao, Min Gong, Chun Jiang, Shulin Sensors (Basel) Article 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. MDPI 2022-02-05 /pmc/articles/PMC8838333/ /pubmed/35161957 http://dx.doi.org/10.3390/s22031214 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Dunwen Chen, Haofei Tang, Yu Liu, Chao Cao, Min Gong, Chun Jiang, Shulin Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title | Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title_full | Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title_fullStr | Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title_full_unstemmed | Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title_short | Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System |
title_sort | slope micrometeorological analysis and prediction based on an arima model and data-fitting system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838333/ https://www.ncbi.nlm.nih.gov/pubmed/35161957 http://dx.doi.org/10.3390/s22031214 |
work_keys_str_mv | AT liudunwen slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT chenhaofei slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT tangyu slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT liuchao slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT caomin slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT gongchun slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem AT jiangshulin slopemicrometeorologicalanalysisandpredictionbasedonanarimamodelanddatafittingsystem |