Assessing River Benue flow data for flood mitigation and management in Adamawa catchment, Nigeria

Olayinka-Dosunmu, Dupe N.; Adzandeh, Ayila; Hamid-Mosaku, Isa Adekunle; Okolie, Chukwuma J.; Nwilo, Peter C.; Ogbeta, Caleb O.

doi:10.1016/j.sciaf.2022.e01205

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The temporal and spatial distribution of river flow has both positive and negative effects on human beings. Studying the characteristics of the temporal and spatial distribution of river flow can help us reduce disadvantages, such as flooding or riverbed scouring, and potentially transform them into advantages for effective utilization [1][2][3]. The curved streamway is one of the most common waterway patterns in nature, and even straight rivers will transition to curved under the influence of various special factors in the natural environment [4].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends.

show abstract