干旱区生态环境知识资源中心(Arid): Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area

Arid

DOI	10.1016/j.agwat.2020.106121
	Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
	Yamac, Sevim Seda 1; Seker, Cevdet 2; Negis, Hamza 2
通讯作者	Yamac, Sevim Seda
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2020
卷号	234
英文摘要	This study evaluated the performance of deep learning (DL), artificial neural network (ANN) and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting point (PWP) using four combinations of soil data. The DL, ANN and kNN models are compared with the previous published pedotransfer functions (PTF). The data consist of 256 calcareous soil samples collected from Konya-Cumra plain, Turkey. The results demonstrated that the DL_a with inputs of soil texture components, bulk density, organic matter and lime contents, particle density and aggregate stability showed the best performances with coefficient of determination (R-2) of 0.829, correlation coefficient (r) of 0.911, mean absolute error (MAE) of 0.027 and relative root mean square error (RRMSE) 9.397 % in FC estimation for calcareous soil samples. For the PWP estimation of calcareous soil samples, the kNN_b with soil texture components, bulk density, organic matter and lime content and particle density indicated the best performance with the value of R-2 to 0.800, of r to 0.894, of MAE to 0.021 and RRMSE to 12.043 %. Lastly, the results showed that the DL, ANN and the kNN models perform better than the previously applied PTF for calcareous soils. Therefore, the DL model could be recommended for the estimation of FC when full soil data are available and the kNN model could be recommended for estimation of PWP with all combinations of soil data.
英文关键词	Field capacity Permanent wilting point Deep learning Artificial neural network k-nearest neighbour
类型	Article
语种	英语
国家	Turkey
收录类别	SCI-E
WOS记录号	WOS:000525293700019
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; REFERENCE EVAPOTRANSPIRATION ESTIMATION ; SUPPORT VECTOR MACHINES ; PEDOTRANSFER FUNCTIONS ; WATER-RETENTION ; FIELD-CAPACITY ; SIZE DISTRIBUTION ; ORGANIC-MATTER ; AGGREGATE-SIZE ; PARTICLE-SIZE
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/313969
作者单位	1.Konya Food & Agr Univ, Fac Agr & Nat Sci, Dept Plant Prod & Technol, Konya, Turkey; 2.Selcuk Univ, Fac Agr, Dept Soil Sci & Plant Nutr, Konya, Turkey
推荐引用方式 GB/T 7714	Yamac, Sevim Seda,Seker, Cevdet,Negis, Hamza. Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area[J],2020,234.
APA	Yamac, Sevim Seda,Seker, Cevdet,&Negis, Hamza.(2020).Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area.AGRICULTURAL WATER MANAGEMENT,234.
MLA	Yamac, Sevim Seda,et al."Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area".AGRICULTURAL WATER MANAGEMENT 234(2020).