TY - JOUR
T1 - Genetic algorithm-assisted data-driven model for boundary shear distribution and stage-discharge
T2 - Compound open channel flows
AU - Kumar Singh, Prateek
AU - Tang, Xiaonan
AU - Guan, Yutong
AU - Rahimi, Hamidreza
N1 - Funding Information:
The authors want to acknowledge the financial support from the National Natural Science Foundation of China (11772270) and XJTLU (RDF-16-02-02, PGRS2012007, REF-20-02-03).
Funding Information:
All authors have read and agreed to the published version of the manuscript. Data used during the study are available from the corresponding author by request. The authors thank the editor and anonymous reviewers for their efforts and time on this paper. Funding:, The authors want to acknowledge the financial support from the National Natural Science Foundation of China (11772270) and XJTLU (RDF-16-02-02, PGRS2012007, REF-20-02-03). Data Availability:, The data presented in this study are available on request from the corresponding author. Furthermore, the authors would also like to sincerely thank all the past researchers who gave valuable experimental datasets. The second author Dr Tang is incredibly grateful to provide datasets of the Flood Channel Facility (FCF), archived for public use. Declarations Ethics Approval and Consent to Participate:, This article does not contain any studies with human participants or animals performed by any authors. Consent to Publish. The authors declare no conflict of interest. Institutional Review Board Statement:, Not applicable.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/12
Y1 - 2022/12
N2 - In this paper, a unified method has been proposed for the percentage shear force carried by floodplains using fewer non-dimensional parameters such as the floodplain's percentage area, the ratio of Manning's roughness and the depth ratio. This new data-driven dynamic model for percentage shear force is obtained using a genetic algorithm (GA) program, a well-documented machine-learning software, which can examine the existing relationship among the variables and explore the influencing factors. GA facilitated a unified relationship between apparent shear force and the chosen parameters. The new proposed model is simple and accurate compared to the previous models, which were either complex or distinct for different configurations. The efficiency of the new model shows that the most predicted test case results are under the 5% error cap. The cohesive expression derived for smooth and roughened compound channels is the most significant advantage of the GA-based data-driven model, providing a simple and easy-use formula for engineers to apply for broad applications. Compared with the other available approaches, the model proposed provides the most accurate discharge prediction for various unique datasets.
AB - In this paper, a unified method has been proposed for the percentage shear force carried by floodplains using fewer non-dimensional parameters such as the floodplain's percentage area, the ratio of Manning's roughness and the depth ratio. This new data-driven dynamic model for percentage shear force is obtained using a genetic algorithm (GA) program, a well-documented machine-learning software, which can examine the existing relationship among the variables and explore the influencing factors. GA facilitated a unified relationship between apparent shear force and the chosen parameters. The new proposed model is simple and accurate compared to the previous models, which were either complex or distinct for different configurations. The efficiency of the new model shows that the most predicted test case results are under the 5% error cap. The cohesive expression derived for smooth and roughened compound channels is the most significant advantage of the GA-based data-driven model, providing a simple and easy-use formula for engineers to apply for broad applications. Compared with the other available approaches, the model proposed provides the most accurate discharge prediction for various unique datasets.
KW - Apparent shear
KW - Boundary shear
KW - Compound open channel flow
KW - Genetic algorithm
KW - Stage-discharge
UR - http://www.scopus.com/inward/record.url?scp=85144271453&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2022.128564
DO - 10.1016/j.jhydrol.2022.128564
M3 - Article
AN - SCOPUS:85144271453
SN - 0022-1694
VL - 615
JO - Journal of Hydrology
JF - Journal of Hydrology
M1 - 128564
ER -