Machine Learning in the Analysis of Carbon Dioxide Flow on a Site with Heterogeneous Vegetation

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-11-01 DOI:10.3390/info14110591
Ekaterina Kulakova, Elena Muravyova
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Abstract

The article presents the results of studies of carbon dioxide flow in the territory of section No. 5 of the Eurasian Carbon Polygon (Russia, Republic of Bashkortostan). The gas analyzer Sniffer4D V2.0 (manufactured in Shenzhen, China) with an installed CO2 sensor, quadrocopter DJI MATRICE 300 RTK (manufactured in Shenzhen, China) were used as control devices. The studies were carried out on a clear autumn day in conditions of green vegetation and on a frosty November day with snow cover. Statistical characteristics of experimental data arrays are calculated. Studies of the influence of temperature, humidity of atmospheric air on the current value of CO2 have been carried out. Graphs of the distribution of carbon dioxide concentration in the atmospheric air of section No. 5 on autumn and winter days were obtained. It has been established that when building a model of CO2 in the air, the parameters of the process of deposition by green vegetation should be considered. It was found that in winter, an increase in air humidity contributes to a decrease in gas concentration. At an ambient temperature of 21 °C, an increase in humidity leads to an increase in the concentration of carbon dioxide.
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机器学习在异质植被场地二氧化碳流量分析中的应用
本文介绍了欧亚碳多边形第5段(俄罗斯,巴什科尔托斯坦共和国)境内二氧化碳流动的研究结果。气体分析仪Sniffer4D V2.0(中国深圳制造),安装二氧化碳传感器,四旋翼机DJI MATRICE 300 RTK(中国深圳制造)作为控制装置。这些研究是在一个晴朗的秋日,在绿色植被的条件下进行的,而在11月一个霜冻的雪天进行的。计算了实验数据阵列的统计特性。研究了大气温度、湿度对CO2电流值的影响。得到了5号断面秋冬两季大气中二氧化碳浓度分布图。建立空气中CO2的模型时,应考虑绿色植被沉积过程的参数。研究发现,在冬季,空气湿度的增加有助于气体浓度的降低。在环境温度为21℃时,湿度的增加会导致二氧化碳浓度的增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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