气候变化领域集成服务门户(CCPortal): Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands

CCPortal

DOI	10.1016/j.jenvman.2024.120335
	Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands
	Jiang, Bi-Ni; Zhang, Ying-Ying; Zhang, Zhi-Yong; Yang, Yu-Li; Song, Hai-Liang
发表日期	2024
ISSN	0301-4797
EISSN	1095-8630
起始页码	354
卷号	354
英文摘要	Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.
英文关键词	TPE optimization; Hyperparameter tuning; Automated machine learning; Biochar; Nitrous oxide
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001200186200001
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302331
作者单位	Nanjing Normal University; Jiangsu Academy of Agricultural Sciences; Ministry of Agriculture & Rural Affairs
推荐引用方式 GB/T 7714	Jiang, Bi-Ni,Zhang, Ying-Ying,Zhang, Zhi-Yong,et al. Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands[J],2024,354.
APA	Jiang, Bi-Ni,Zhang, Ying-Ying,Zhang, Zhi-Yong,Yang, Yu-Li,&Song, Hai-Liang.(2024).Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands.JOURNAL OF ENVIRONMENTAL MANAGEMENT,354.
MLA	Jiang, Bi-Ni,et al."Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N2O mitigation effect in constructed wetlands".JOURNAL OF ENVIRONMENTAL MANAGEMENT 354(2024).