干旱区生态环境知识资源中心(Arid): Multiple AI model integration strategy-Application to saturated hydraulic conductivity prediction from easily available soil properties

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DOI	10.1016/j.still.2019.104449
	Multiple AI model integration strategy-Application to saturated hydraulic conductivity prediction from easily available soil properties
	Kashani, Mahsa H.1; Ghorbani, Mohammad Ali 2,3; Shahabi, Mahmood 4; Naganna, Sujay Raghavendra 5,6; Diop, Lamine 7
通讯作者	Kashani, Mahsa H.
来源期刊	SOIL & TILLAGE RESEARCH
ISSN	0167-1987
EISSN	1879-3444
出版年	2020
卷号	196
英文摘要	A multiple model integration scheme driven by artificial neural network (ANN) (MM-ANN) was developed and tested to improve the prediction accuracy of soil hydraulic conductivity (Ks) in Tabriz plain, an arid region of Iran. The soil parameters such as silt, clay, organic matter (OM), bulk density (BD), pH and electrical conductivity (EC) were used as model inputs to predict soil Ks. Standalone models including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), support vector machine (SVM) and extreme learning machine (ELM) were also implemented for comparative evaluation with MM-ANN model predictions. Based on several performance indicators such as Nash Sutcliffe Efficiency (NSE), results showed that the calibrated MMANN model involving the predictions of MARS, M5Tree, SVM and ELM models by considering all the soil parameters used in this study as inputs provided superior soil Ks estimates. The proposed hybrid model (MM-ANN) emerged as a reliable intelligence model for the assessment of soil hydraulic conductivity with an NSE = 0.939 & 0.917 during training and testing, respectively. Accurate prediction of field-scale soil hydraulic conductivity is crucial from the view point of agricultural sustainability and management prospects.
英文关键词	Saturated hydraulic conductivity Extreme learning machine Multiple model strategy Multivariate adaptive regression splines M5Tree Support vector machine Prediction
类型	Article
语种	英语
国家	Iran ; Turkey ; India ; Senegal
收录类别	SCI-E
WOS记录号	WOS:000501416400030
WOS关键词	SUPPORT VECTOR MACHINE ; ADAPTIVE REGRESSION SPLINES ; ARTIFICIAL NEURAL-NETWORKS ; EXTREME LEARNING-MACHINE ; PAN EVAPORATION ; PEDOTRANSFER FUNCTIONS ; EXPERIMENTAL-DESIGN ; WATER-RETENTION ; TRACER TEST ; STREAM
WOS类目	Soil Science
WOS研究方向	Agriculture
EI主题词	2020-02-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312693
作者单位	1.Univ Mohaghegh Ardabili, Fac Agr & Nat Resources, Dept Water Engn, Ardebil, Iran; 2.Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz, Iran; 3.Near East Univ, Dept Civil Engn, POB 99138,Mersin 10, Nicosia, North Cyprus, Turkey; 4.Univ Tabriz, Fac Agr, Dept Soil Sci, Tabriz, Iran; 5.Shri Madhwa Vadiraja Inst Technol & Management, Dept Civil Engn, Bantakal 574115, Udupi, India; 6.Visvesvaraya Technol Univ, Belagavi 590018, India; 7.Univ Gaston Berger, St Louis, Senegal
推荐引用方式 GB/T 7714	Kashani, Mahsa H.,Ghorbani, Mohammad Ali,Shahabi, Mahmood,et al. Multiple AI model integration strategy-Application to saturated hydraulic conductivity prediction from easily available soil properties[J],2020,196.
APA	Kashani, Mahsa H.,Ghorbani, Mohammad Ali,Shahabi, Mahmood,Naganna, Sujay Raghavendra,&Diop, Lamine.(2020).Multiple AI model integration strategy-Application to saturated hydraulic conductivity prediction from easily available soil properties.SOIL & TILLAGE RESEARCH,196.
MLA	Kashani, Mahsa H.,et al."Multiple AI model integration strategy-Application to saturated hydraulic conductivity prediction from easily available soil properties".SOIL & TILLAGE RESEARCH 196(2020).