scholarly journals Scour Hole Development in Natural Cohesive Bed Sediment around Cylinder-Shaped Piers Subjected to Varying Sequential Flow Events

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3289
Author(s):  
Badal Mahalder ◽  
John S. Schwartz ◽  
Angelica M. Palomino ◽  
Jon Zirkle
Keyword(s):  
Flow Velocity ◽  
Bulk Density ◽  
Soil Bulk Density ◽  
Erosion Rates ◽  
Riverbed Sediments ◽  
Velocity Flow ◽  
Scour Hole ◽  
Process Feedback ◽  
Cohesive Bed ◽  
Single Flow

Scour evolution and propagation around a cylinder in natural cohesive sediment was uniquely investigated under multi-flow event varying sequentially by velocity magnitudes. This flume study differs from others that only used test sediment with commercially available clays for single flow. The objective of this study was to explore the potential differences in scour hole development in natural riverbed sediments subjected to varying flow velocity scenarios, advancing our understanding from existing studies on scour. The study consisted of 18 experimental runs based on: velocity, flow duration, and soil bulk density. Scour hole development progressed initially along the cylinder sides, and maximum depths also occurred at these lateral locations. Scour hole depths were less for higher soil bulk densities (≥1.81 g/cm3) compared with lower densities, and erosion rates were slower. It was observed with all flow sequences that scour depths were similar at the end of each experimental run. However, scour initiation was observed to be time dependent for soils with higher bulk density (1.81–2.04 g/cm3) regardless of flow velocity sequences. The observed time dependency suggests a process feedback with the scour hole development initiated at the cylinder sides, which influence local 3D hydraulics as the scour hole depth progresses.

MEASUREMENT AND PALEOCLIMATIC SIGNIFICANCE OF SOIL BULK DENSITY FOR LOESS IN EXTREME ARID REGION

2010 ◽  
Vol 30 (2) ◽  
pp. 127-132
Author(s):  
Jinbo ZAN ◽  
Shengli YANG ◽  
Xiaomin FANG ◽  
Xiangyu LI ◽  
Yibo YANG ◽  
...  
Keyword(s):  
Bulk Density ◽  
Arid Region ◽  
Soil Bulk Density ◽  
Paleoclimatic Significance

scholarly journals Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4408
Author(s):  
Iman Salehi Hikouei ◽  
S. Sonny Kim ◽  
Deepak R. Mishra
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Salt Marsh ◽  
Bulk Density ◽  
Remote Sensing Data ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Marsh Soil ◽  
Landsat 7 ◽  
Salt Marsh Soil

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Measurement of Soil Bulk Density and Water Content with Time Domain Reflectometry: Algorithm Implementation and Method Analysis

2021 ◽  
pp. 126389
Author(s):  
Marco Bittelli ◽  
Fausto Tomei ◽  
Anbazhagan P. ◽  
Raghuveer Rao Pallapati ◽  
Puskar Mahajan ◽  
...  
Keyword(s):  
Water Content ◽  
Bulk Density ◽  
Time Domain ◽  
Soil Bulk Density ◽  
Time Domain Reflectometry ◽  
Algorithm Implementation ◽  
Method Analysis
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