干旱区生态环境知识资源中心(Arid): Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions

Arid

DOI	10.1016/j.catena.2022.106459
	Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions
	Albalasmeh, Ammar; Mohawesh, Osama; Gharaibeh, Mamoun; Deb, Sanjit; Slaughter, Lindsey; El Hanandeh, Ali
通讯作者	Mohawesh, O ; El Hanandeh, A
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2022
卷号	217
英文摘要	Saturated hydraulic conductivity (K-sat), one of the critical soil hydraulic properties, is used to model many soil hydrological processes. Measurement of K-sat on a routine basis is a labor-intensive, time-consuming, and expensive process. Alternatively, prediction of K-sat values from easy to obtain soil features is more economical and saves time. Artificial neural networks (ANNs) can be used to model and describe the most influential features affecting K-sat. This study aimed to develop and evaluate the potential use of generalized regression neural network (GRNN) to identify the optimal set of soil features to predict K-sat under arid and semi-arid environments. A total of 165 soil samples were collected from three depths (0-15, 15-30, and 30-60 cm) and analyzed for K-sat, texture, organic matter (OM), pH, bulk density (BD), and electrical conductivity (EC). Fourteen GRNN models were built with different feature combinations to identify the optimal set to predict K-sat. The results showed that soil texture explained 78% of the variability in soil K-sat while introducing EC improved model's ability to estimate soil Ksat (R = 0.93, MSE = 2.89 x 10(-12) m2 S-2). The optimum set of soil properties that should be included in the model were sand and clay percentages and EC values as evidenced from the cross-validation results. The GRNN model (using small dataset and set of features) provided reliable predictions of Ksat on bar with more complex models that included extensive set of features and used more extensive dataset. This work has implications for soil scients as provides an economical method to estimate K-sat values.
英文关键词	Arid region GRNN Saturated hydraulic conductivity Jordan Valley Artificial intelligence
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000822981200003
WOS关键词	SOIL-WATER RETENTION ; PEDOTRANSFER FUNCTIONS ; ORGANIC-MATTER ; EVAPOTRANSPIRATION ; CURVES ; RUNOFF ; CARBON
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392109
推荐引用方式 GB/T 7714	Albalasmeh, Ammar,Mohawesh, Osama,Gharaibeh, Mamoun,et al. Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions[J],2022,217.
APA	Albalasmeh, Ammar,Mohawesh, Osama,Gharaibeh, Mamoun,Deb, Sanjit,Slaughter, Lindsey,&El Hanandeh, Ali.(2022).Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions.CATENA,217.
MLA	Albalasmeh, Ammar,et al."Artificial neural network optimization to predict saturated hydraulic conductivity in arid and semi-arid regions".CATENA 217(2022).