Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning
The rapid increase in atmospheric CO2 concentration has caused a climate disaster (CO2 disaster). This study expands the theory for reducing this disaster by analyzing the possibility of reinforcing soil CO2 uptake (Fx) in arid regions using partial least-squares regression (PLSR) and machine learni...
| Váldodahkkit: | , , , |
|---|---|
| Materiálatiipa: | Artihkal |
| Giella: | English |
| Almmustuhtton: |
Frontiers Media S.A.
2022-09-01
|
| Ráidu: | Frontiers in Earth Science |
| Fáttát: | |
| Liŋkkat: | https://www.frontiersin.org/articles/10.3389/feart.2022.1004920/full |
| _version_ | 1828239318912598016 |
|---|---|
| author | Bai-Zhou Xu Xiao-Liang Li Wen-Feng Wang Xi Chen Xi Chen Xi Chen Xi Chen |
| author_facet | Bai-Zhou Xu Xiao-Liang Li Wen-Feng Wang Xi Chen Xi Chen Xi Chen Xi Chen |
| author_sort | Bai-Zhou Xu |
| collection | DOAJ |
| description | The rapid increase in atmospheric CO2 concentration has caused a climate disaster (CO2 disaster). This study expands the theory for reducing this disaster by analyzing the possibility of reinforcing soil CO2 uptake (Fx) in arid regions using partial least-squares regression (PLSR) and machine learning models such as artificial neural networks. The results of this study demonstrated that groundwater level is a leading contributor to the regulation of the dynamics of the main drivers of Fx–air temperature at 10 cm above the soil surface, the soil volumetric water content at 0–5 cm (R2=0.76, RMSE=0.435), and soil pH (R2=0.978, RMSE=0.028) in arid regions. Fx can be reinforced through groundwater source management which influences the groundwater level (R2=0.692, RMSE=0.03). This study also presents and discusses some basic hypotheses and evidence for quantitively reinforcing Fx. |
| first_indexed | 2024-04-12T21:20:46Z |
| format | Article |
| id | doaj.art-cf46fbd22bfe4393bca99e70d53963a2 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-12T21:20:46Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-cf46fbd22bfe4393bca99e70d53963a22022-12-22T03:16:18ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632022-09-011010.3389/feart.2022.10049201004920Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learningBai-Zhou Xu0Xiao-Liang Li1Wen-Feng Wang2Xi Chen3Xi Chen4Xi Chen5Xi Chen6School of Computer Science and Technology, Hainan University, Haikou, ChinaJiyang College, Zhejiang A&F University, Zhuji, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaSino-Belgian Joint Laboratory of Geo-information, Urumqi, ChinaCAS Research Centre for Ecology and Environment of Central Asia, Urumqi, ChinaThe rapid increase in atmospheric CO2 concentration has caused a climate disaster (CO2 disaster). This study expands the theory for reducing this disaster by analyzing the possibility of reinforcing soil CO2 uptake (Fx) in arid regions using partial least-squares regression (PLSR) and machine learning models such as artificial neural networks. The results of this study demonstrated that groundwater level is a leading contributor to the regulation of the dynamics of the main drivers of Fx–air temperature at 10 cm above the soil surface, the soil volumetric water content at 0–5 cm (R2=0.76, RMSE=0.435), and soil pH (R2=0.978, RMSE=0.028) in arid regions. Fx can be reinforced through groundwater source management which influences the groundwater level (R2=0.692, RMSE=0.03). This study also presents and discusses some basic hypotheses and evidence for quantitively reinforcing Fx.https://www.frontiersin.org/articles/10.3389/feart.2022.1004920/fullCO2 disasterpartial least-squares regression (PLSR)artificial neural network (ANN)desert systemsenvironmental controls |
| spellingShingle | Bai-Zhou Xu Xiao-Liang Li Wen-Feng Wang Xi Chen Xi Chen Xi Chen Xi Chen Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning Frontiers in Earth Science CO2 disaster partial least-squares regression (PLSR) artificial neural network (ANN) desert systems environmental controls |
| title | Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning |
| title_full | Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning |
| title_fullStr | Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning |
| title_full_unstemmed | Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning |
| title_short | Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning |
| title_sort | expanding the theory for reducing the co2 disaster hypotheses from partial least squares regression and machine learning |
| topic | CO2 disaster partial least-squares regression (PLSR) artificial neural network (ANN) desert systems environmental controls |
| url | https://www.frontiersin.org/articles/10.3389/feart.2022.1004920/full |
| work_keys_str_mv | AT baizhouxu expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT xiaoliangli expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT wenfengwang expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT xichen expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT xichen expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT xichen expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning AT xichen expandingthetheoryforreducingtheco2disasterhypothesesfrompartialleastsquaresregressionandmachinelearning |