Spatiotemporal variation of sedimentation in an agricultural drainage canal with eco-friendly physical structures: a case study

2020 ◽  
Author(s):  
Shigeya Maeda ◽  
Shota Takagi ◽  
Koshi Yoshida ◽  
Hisao Kuroda
Keyword(s):  
Spatiotemporal Variation ◽  
Agricultural Drainage ◽  
Drainage Canal

scholarly journals Application of Two Shallow Water Models to Steady Flow Analysis in a Vegetated Agricultural Drainage Canal

2013 ◽  
Vol 19 (2) ◽  
pp. 35-41 ◽  
Author(s):  
Hidekazu Yoshioka ◽  
Nobuhiko Kinjo ◽  
Ayaka Wakazono ◽  
Koichi Unami ◽  
Masayuki Fujihara
Keyword(s):  
Shallow Water ◽  
Steady Flow ◽  
Flow Analysis ◽  
Agricultural Drainage ◽  
Drainage Canal ◽  
Shallow Water Models ◽  
Water Models

scholarly journals Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure

2018 ◽  
Vol 10 (12) ◽  
pp. 4564 ◽  
Author(s):  
Zhuangbin Shi ◽  
Ning Zhang ◽  
Yang Liu ◽  
Wei Xu
Keyword(s):  
Built Environment ◽  
Topological Structure ◽  
Regression Models ◽  
Closeness Centrality ◽  
Spatiotemporal Variation ◽  
Spatial And Temporal Variation ◽  
Structure Factors ◽  
The Relationship ◽  
Best Fit

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document