Abstract
The location and characterization of water runoff locations in canal networks is of critical importance in order to properly forecast and manage floods. Heavy rains in upstream areas can suddenly increase the rate of discharge resulting in events such as overflow, seepage losses, erosion, or flooding. The inability to simulate floods in the actual terrain often results in the actual floods developing in unexpected patterns. Thus, actual floods have been documented to occur in the inhabited side of a canal as opposed to the uninhabited embankment where managers had planned to occur. Also, warning alarms have only triggered once the flood had occurred. Additionally, nature- and human-made activities (e.g., driving trucks or cars) result in the loss of soil, creation of uneven surfaces, and erosion of edges on the canal embankment, and, overall, change the embankment profile and can alter the water runoff outlet over time. Currently, though, the manual localization of water runoff escape points is often overseen in large infrastructure networks since it demands a time-consuming, labor intensive, and prone-to-error surveying effort. The efforts in the ongoing study presented in this paper introduce a methodology to automatically detect the lowest points along canal embankments. High-resolution raster images and 3D point cloud representation of the existing canal infrastructure and surrounding areas, produced with above-the-ground photogrammetric sensors, are collected along the canals. Then, geometric algorithm, such as random sample consensus (RANSAC) is used to analyze the sensed data. This paper presents the preliminary results of an ongoing research study, showing the elevation and coordinates for the lowest and near-lowest escape outlets. Such results promise to minimize soil erosion and improve the predictability and effectiveness of flood monitoring approaches.
| Original language | English (US) |
|---|---|
| Title of host publication | Construction Research Congress 2018 |
| Subtitle of host publication | Infrastructure and Facility Management - Selected Papers from the Construction Research Congress 2018 |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 760-769 |
| Number of pages | 10 |
| Volume | 2018-April |
| ISBN (Electronic) | 9780784481295 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | Construction Research Congress 2018: Infrastructure and Facility Management, CRC 2018 - New Orleans, United States Duration: Apr 2 2018 → Apr 4 2018 |
Other
| Other | Construction Research Congress 2018: Infrastructure and Facility Management, CRC 2018 |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 4/2/18 → 4/4/18 |
Fingerprint
ASJC Scopus subject areas
- Building and Construction
- Civil and Structural Engineering
Cite this
Automated Water Runoff Location in Large Canal Networks. / Krishna Paladugu, Bala Sai; Grau Torrent, David.
Construction Research Congress 2018: Infrastructure and Facility Management - Selected Papers from the Construction Research Congress 2018. Vol. 2018-April American Society of Civil Engineers (ASCE), 2018. p. 760-769.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automated Water Runoff Location in Large Canal Networks
AU - Krishna Paladugu, Bala Sai
AU - Grau Torrent, David
PY - 2018/1/1
Y1 - 2018/1/1
N2 - The location and characterization of water runoff locations in canal networks is of critical importance in order to properly forecast and manage floods. Heavy rains in upstream areas can suddenly increase the rate of discharge resulting in events such as overflow, seepage losses, erosion, or flooding. The inability to simulate floods in the actual terrain often results in the actual floods developing in unexpected patterns. Thus, actual floods have been documented to occur in the inhabited side of a canal as opposed to the uninhabited embankment where managers had planned to occur. Also, warning alarms have only triggered once the flood had occurred. Additionally, nature- and human-made activities (e.g., driving trucks or cars) result in the loss of soil, creation of uneven surfaces, and erosion of edges on the canal embankment, and, overall, change the embankment profile and can alter the water runoff outlet over time. Currently, though, the manual localization of water runoff escape points is often overseen in large infrastructure networks since it demands a time-consuming, labor intensive, and prone-to-error surveying effort. The efforts in the ongoing study presented in this paper introduce a methodology to automatically detect the lowest points along canal embankments. High-resolution raster images and 3D point cloud representation of the existing canal infrastructure and surrounding areas, produced with above-the-ground photogrammetric sensors, are collected along the canals. Then, geometric algorithm, such as random sample consensus (RANSAC) is used to analyze the sensed data. This paper presents the preliminary results of an ongoing research study, showing the elevation and coordinates for the lowest and near-lowest escape outlets. Such results promise to minimize soil erosion and improve the predictability and effectiveness of flood monitoring approaches.
AB - The location and characterization of water runoff locations in canal networks is of critical importance in order to properly forecast and manage floods. Heavy rains in upstream areas can suddenly increase the rate of discharge resulting in events such as overflow, seepage losses, erosion, or flooding. The inability to simulate floods in the actual terrain often results in the actual floods developing in unexpected patterns. Thus, actual floods have been documented to occur in the inhabited side of a canal as opposed to the uninhabited embankment where managers had planned to occur. Also, warning alarms have only triggered once the flood had occurred. Additionally, nature- and human-made activities (e.g., driving trucks or cars) result in the loss of soil, creation of uneven surfaces, and erosion of edges on the canal embankment, and, overall, change the embankment profile and can alter the water runoff outlet over time. Currently, though, the manual localization of water runoff escape points is often overseen in large infrastructure networks since it demands a time-consuming, labor intensive, and prone-to-error surveying effort. The efforts in the ongoing study presented in this paper introduce a methodology to automatically detect the lowest points along canal embankments. High-resolution raster images and 3D point cloud representation of the existing canal infrastructure and surrounding areas, produced with above-the-ground photogrammetric sensors, are collected along the canals. Then, geometric algorithm, such as random sample consensus (RANSAC) is used to analyze the sensed data. This paper presents the preliminary results of an ongoing research study, showing the elevation and coordinates for the lowest and near-lowest escape outlets. Such results promise to minimize soil erosion and improve the predictability and effectiveness of flood monitoring approaches.
UR - http://www.scopus.com/inward/record.url?scp=85048942696&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048942696&partnerID=8YFLogxK
U2 - 10.1061/9780784481295.076
DO - 10.1061/9780784481295.076
M3 - Conference contribution
AN - SCOPUS:85048942696
VL - 2018-April
SP - 760
EP - 769
BT - Construction Research Congress 2018
PB - American Society of Civil Engineers (ASCE)
ER -