Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models
文献类型：期刊论文
| 作者 | Qi, Yanbing1,2; Huo, Zailin1; Feng, Shaoyuan3; Adeloye, Adebayo J.4; Dai, Xiaoqin5
|
| 刊名 | IRRIGATION AND DRAINAGE
 |
| 出版日期 | 2018-10-01 |
| 卷号 | 67期号:4页码:615-624 |
| ISSN号 | 1531-0353 |
| 关键词 | crop water consumption soil moisture salinity artificial neural network |
| DOI | 10.1002/ird.2270 |
| 通讯作者 | Huo, Zailin(huozl@cau.edu.cn) |
| 英文摘要 | The response of the water use of crops to soil moisture and salinity is complex to quantify using traditional field experiments. Based on field experimental data for 2years, artificial neural network (ANN) models with five inputs including soil moisture content, total salt content, plant height, leaf area index and crop reference evapotranspiration (ET0) were developed to estimate daily actual evapotranspiration (ET). The models were later used to simulate the response of crop water consumption to soil moisture and salinity stresses at different growth stages. The results showed that the ANN model has a high precision with root mean squared error of 0.41 and 0.52mmday(-1), relative error of 19.6 and 25.6%, and coefficient of determination of 0.87 and 0.79 for training and testing samples, respectively. Furthermore, the simulation results showed that the seed corn ET is sensitive to soil salt stress at all growth stages, although the salinity threshold at which the impact becomes felt and the extent of the impact vary for the different growth stages, with the booting and tasseling stages being the most robust. The study offers a more direct approach of evaluating actual crop evapotranspiration by considering explicitly water and salinity stresses. (c) 2018 John Wiley & Sons, Ltd. |
| WOS关键词 | REFERENCE EVAPOTRANSPIRATION ; DEFICIT IRRIGATION ; WATER PRODUCTIVITY ; NORTHWEST CHINA ; YIELD ; WHEAT ; RIVER ; FIELD |
| 资助项目 | National Key R&D Program of China[2016YFC0400107] ; National Natural Science Foundation of China[516390095167923691425302] |
| WOS研究方向 | Agriculture ; Water Resources |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:000446663100013 |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/52735]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huo, Zailin |
| 作者单位 | 1.China Agr Univ, Ctr Agr Water Res China, 17 Qinghua East Rd, Beijing 100083, Peoples R China 2.Beijing Water Sci & Technol Inst, Beijing, Peoples R China 3.Yangzhou Univ, Sch Hydraul Energy & Power Engn, Yangzhou, Jiangsu, Peoples R China 4.Heriot Watt Univ, Inst Infrastruct & Environm, Edinburgh, Midlothian, Scotland 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qi, Yanbing,Huo, Zailin,Feng, Shaoyuan,et al. Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models[J]. IRRIGATION AND DRAINAGE,2018,67(4):615-624. |
| APA | Qi, Yanbing,Huo, Zailin,Feng, Shaoyuan,Adeloye, Adebayo J.,&Dai, Xiaoqin.(2018).Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models.IRRIGATION AND DRAINAGE,67(4),615-624. |
| MLA | Qi, Yanbing,et al."Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models".IRRIGATION AND DRAINAGE 67.4(2018):615-624. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。