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DOI	10.1007/s12046-022-01805-6
	Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India
	More, Satish Bhaurao; Deka, Paresh Chandra; Patil, Amit Prakash; Naganna, Sujay Raghavendra
通讯作者	Patil, AP (corresponding author)，Annasaheb Dange Coll Engn & Technol, Dept Civil Engn, Ashta 416301, Maharashtra, India.
来源期刊	SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
ISSN	0256-2499
EISSN	0973-7677
出版年	2022
卷号	47 期号:1
英文摘要	Saturated hydraulic conductivity (K-fs) is the major parameter that affects the movement of water and solutes in soil strata. Although one can estimate the K-fs directly by using various field or laboratory methods, they turn out to be more time-consuming and painstaking while characterizing the spatial variability of K-fs. For this reason, some recent researches employ indirect approaches such as pedotransfer functions (PTF) and surface modeling methods for estimating K-fs of several scales. Pedotransfer functions are often developed by relating the K-fs with readily available soil properties such as bulk density, porosity, sand content, silt content, and organic material. The present research explores the suitability of Extreme Learning Machine (ELM) in developing PTF's for K-fs by using basic soil properties. In-situ field tests and laboratory experiments on collected samples were performed to acquire the datasets necessary for the analysis. Three competitive soft computing approaches, namely the ELM, Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) based on Fuzzy C-means Clustering optimized by Genetic Algorithm were exercised for developing the K-fs models. Further, the performance of these approaches in modeling K-fs was evaluated using various statistical mertics. The performance of ELM was found to be good in comparison to the other two models, with sufficiently good NSE values. The ELM model provided K-fs predictions at the Murarji Peth and Punanaka sites with an NSE of 0.90 and 0.83, respectively, while at the Mulegoan site, the ANFIS model was better with R = 0.80 and NSE = 0.64.
英文关键词	Saturated hydraulic conductivity Guelph permeameter ELM SVM ANFIS
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000747002800001
WOS关键词	PARTICLE-SIZE DISTRIBUTION ; SUPPORT VECTOR MACHINE ; PEDOTRANSFER FUNCTIONS ; SPATIAL VARIABILITY ; FIELD ; SCALE
WOS类目	Engineering, Multidisciplinary
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376254
作者单位	[More, Satish Bhaurao] NK Orchid Coll Engn & Technol, Dept Civil Engn, Solapur 413002, Maharashtra, India; [Deka, Paresh Chandra] Natl Inst Technol Karnataka, Dept Water Resources & Ocean Engn, Mangalore 575025, India; [Patil, Amit Prakash] Annasaheb Dange Coll Engn & Technol, Dept Civil Engn, Ashta 416301, Maharashtra, India; [Naganna, Sujay Raghavendra] Siddaganga Inst Technol, Dept Civil Engn, Tumakuru 572103, Karnataka, India
推荐引用方式 GB/T 7714	More, Satish Bhaurao,Deka, Paresh Chandra,Patil, Amit Prakash,et al. Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India[J],2022,47(1).
APA	More, Satish Bhaurao,Deka, Paresh Chandra,Patil, Amit Prakash,&Naganna, Sujay Raghavendra.(2022).Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India.SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES,47(1).
MLA	More, Satish Bhaurao,et al."Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India".SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 47.1(2022).