干旱区生态环境知识资源中心(Arid): Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India

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DOI	10.1007/s11356-022-20837-3
	Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India
	Malik, Anurag; Tikhamarine, Yazid; Sihag, Parveen; Shahid, Shamsuddin; Jamei, Mehdi; Karbasi, Masoud
通讯作者	Malik, A
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2022
卷号	29 期号:47 页码:71270-71289
英文摘要	Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVMSMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.
英文关键词	Soil temperature Climatic parameters Gamma test Machine learning techniques Punjab
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000798404700010
WOS关键词	PAN-EVAPORATION ESTIMATION ; OPTIMIZATION ALGORITHM ; SOLAR-RADIATION ; NEURAL-NETWORKS ; GAMMA-TEST ; PERFORMANCE ; ANFIS ; SVM
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392527
推荐引用方式 GB/T 7714	Malik, Anurag,Tikhamarine, Yazid,Sihag, Parveen,et al. Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India[J],2022,29(47):71270-71289.
APA	Malik, Anurag,Tikhamarine, Yazid,Sihag, Parveen,Shahid, Shamsuddin,Jamei, Mehdi,&Karbasi, Masoud.(2022).Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,29(47),71270-71289.
MLA	Malik, Anurag,et al."Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 29.47(2022):71270-71289.