Instytut Budownictwa Wodnego
Polskiej Akademii Nauk

Essay #9132 details

ATTRIBUTEVALUE
typeA
database id9132
titlePrediction of hydro-suction dredging depth using data-driven methods
authorsMahdavi-Meymand A.1, Zounemat-Kermani M.1, Qaderi K.1
affiliations
pages652 — 664
DOI10.1007/s11709-021-0719-7
keywordssedimentation, water resources, dam engineering, machine learning, heuristic
abstractsIn this study, data-driven methods (DDMs) including different kinds of group method of data handling (GMDH) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (hs). Sixty-seven experiments were conducted under different hydraulic conditions to measure the hs. Also, 33 data samples from three previous studies were used. The model input variables consisted of pipeline diameter (d), the distance between the pipe inlet and sediment level (Z), the velocity of flow passing through the pipeline (u0), the water head (H), and the medium size of particles (D50). Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better than the PSO algorithm, whereas the PSO algorithm trained simple simulation equations more precisely. Among all used DDMs, the integrative GMDH-HGSO algorithm provided the highest accuracy (RMSE = 7.086 mm). The results also showed that the integrative GMDHs enhance the accuracy of polynomial GMDHs by ∼14.65% (based on the RMSE).
attributes [reviewed] [scientific]
languageen
PART OF
typeC
database id9131
year2021
seriesFrontiers of Structural and Civil Engineering
issue15

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