Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR019533 |
An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method | |
Lamorski, Krzysztof1; Simunek, Jiri2; Slawinski, Cezary1; Lamorska, Joanna3 | |
2017-02-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:2 |
文章类型 | Article |
语种 | 英语 |
国家 | Poland; USA |
英文摘要 | In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE=50.018 m(3)/m(3)) that are significantly lower than those for the M77 (RMSE=50.025 m(3)/m(3)) or KP87 (RMSE=0.047 m(3)/m(3)) models. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000398568800030 |
WOS关键词 | SUPPORT VECTOR MACHINES ; PEDOTRANSFER FUNCTIONS ; SIMILARITY HYPOTHESIS ; CAPILLARY HYSTERESIS ; HYDRAULIC-PROPERTIES ; POROUS-MEDIA ; PORE-NETWORK ; MODEL ; MOISTURE ; PARAMETERS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20823 |
专题 | 资源环境科学 |
作者单位 | 1.Polish Acad Sci, Inst Agrophys, Lublin, Poland; 2.Univ Calif Riverside, Dept Environm Sci, Riverside, CA 92521 USA; 3.State Sch Higher Educ Chelm, Inst Agr Sci, Chelm, Poland |
推荐引用方式 GB/T 7714 | Lamorski, Krzysztof,Simunek, Jiri,Slawinski, Cezary,et al. An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method[J]. WATER RESOURCES RESEARCH,2017,53(2). |
APA | Lamorski, Krzysztof,Simunek, Jiri,Slawinski, Cezary,&Lamorska, Joanna.(2017).An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method.WATER RESOURCES RESEARCH,53(2). |
MLA | Lamorski, Krzysztof,et al."An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method".WATER RESOURCES RESEARCH 53.2(2017). |
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