中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 陆地水循环及地表过程重点实验室
  5. 外文论文
Laboratory channel widening quantification using deep learning

文献类型：期刊论文

作者Wang, Ziyi7; Liu, Haifei7; Qin, Chao5,6; Wells, Robert R.4; Cao, Liekai3,5; Xu, Ximeng2; Momm, Henrique G.1; Zheng, Fenli8
刊名GEODERMA
出版日期2024-10-01
卷号450页码:13
关键词Linear erosion channel Channel expansion Failure block Sediment discharge Channel edge detection
ISSN号0016-7061
DOI10.1016/j.geoderma.2024.117034
产权排序6
英文摘要Linear erosion channel (LEC) devastates arable land and significantly contributes to soil loss in agricultural watersheds. In the presence of a less- or non-erodible layer, channel widening governs the erosion process once the channel bed incises to this layer, accompanied by failure block generation and transport. Current knowledge on channel widening, however, is limited due to the lack of robust and efficient methods to capture the rapid sidewall expansion process. Laboratory experiments were designed to simulate the channel widening process with an initial channel width of 10 cm. Two packed soil beds with a non-erodible layer and two slope gradients (5 % and 11 %) were subjected to the inflow rate of 0.67 L/s. Images were captured by mounted digital cameras and automatically transformed into orthophotos. Channel edges and failure blocks were automatically detected by deep learning algorithm in a newly developed Channel-DeepLab network model based upon DeepLabv3+ + platform. The procedure includes learning samples labelling, data augmentation, model construction, training, and validation. Sediment discharge and changes in channel width, geometry of channel edges, and failure blocks were measured. The results indicate that initial period is critical for erosion prediction and remediation due to its small sidewall failure interval, high channel expansion rate and sediment discharge. Channel surface area has great potential on accumulated sediment discharge prediction. The slope section that witnessed the fastest channel widening rate migrated downwards when slope gradient increased from 5 % to 11 %. The total number and area of the failure blocks increased with time, while the collapse frequency of the sidewalls decreased. Upstream reach experienced the highest sidewall collapse frequency and rate of disaggregation and transport, while the downstream reach experienced the highest total number of failure blocks. A time lag was found between sidewall collapse and sediment discharge, which increased as time progressed, attributing to decreased runoff erosivity as the flow velocity decreased. Results of this study will provide methodological support for channel sidewall and streambank retreat monitoring, realizing the automatic detection of channel edges and efficient output of rapid sidewall expansion process with high temporal and spatial precision. Future work can be focused on broadening the applicability of the Channel-DeepLab network model and quantifying the delayed response process between sidewall failure and sediment discharge.
WOS关键词GULLY EROSION ; BANK EROSION ; WIDTH ; SIMULATION ; RAINFALL ; PREDICTION ; IMPACTS ; MODEL ; RATES ; FLOW
资助项目USDA-ARS-National Sedimentation Laboratory scientists in support of USDA-ARS Research Cooperative Agreement[0060511] ; National Key Research and Development Program of China[2023YFC3209003] ; National Natural Science Foundation of China[U2243236] ; National Natural Science Foundation of China[U2243226] ; National Natural Science Foundation of China[52009061] ; Yellow River Basin Ecological Protection and High-quality Development Joint Study[2022-YRUC-01-0202] ; Postdoctoral Innovation Talents Support Program of China[BX20190177]
WOS研究方向Agriculture
语种英语
WOS记录号WOS:001321660500001
出版者ELSEVIER
资助机构USDA-ARS-National Sedimentation Laboratory scientists in support of USDA-ARS Research Cooperative Agreement ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Yellow River Basin Ecological Protection and High-quality Development Joint Study ; Postdoctoral Innovation Talents Support Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/210111]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Liu, Haifei
作者单位1.Middle Tennessee State Univ, Dept Geosci, Murfreesboro, TN 37130 USA
2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resource Res, Beijing 100101, Peoples R China
3.North China Elect Power Univ, Sch Water Resources & Hydropower Engn, Beijing 102206, Peoples R China
4.USDA ARS, Natl Sedimentat Lab, Oxford, MS 38655 USA
5.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China
6.Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Shaanxi, Peoples R China
7.Beijing Normal Univ, Sch Environm, Beijing 100875, Peoples R China
8.Northwest A&F Univ, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Ziyi,Liu, Haifei,Qin, Chao,et al. Laboratory channel widening quantification using deep learning[J]. GEODERMA,2024,450:13.
APA Wang, Ziyi.,Liu, Haifei.,Qin, Chao.,Wells, Robert R..,Cao, Liekai.,...&Zheng, Fenli.(2024).Laboratory channel widening quantification using deep learning.GEODERMA,450,13.
MLA Wang, Ziyi,et al."Laboratory channel widening quantification using deep learning".GEODERMA 450(2024):13.

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。