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dc.contributor.authorHuang, Li-Jeng
dc.contributor.authorHsiao, Darn-Horng
dc.date.accessioned2019-08-21T15:59:33Z
dc.date.available2019-08-21T15:59:33Z
dc.date.issued2019
dc.description.abstractFive machine learning techniques-- classical nonlinear regression (NLR), multi-layer perceptrons (MLP), support vector machines (SVM) with radial-basis function (RBF) kernel, k nearest neighbour (kNN) and decision tree (DT) schemes-- were applied for regression of velocity distribution along the depth of debris flows by using experimental data of steady uniform open-channel flows. Programs coded in Python and package scikit-learn were developed for machine learning analyses. Experimental results of two cases conducted and published by Matsumura and Mizuyama (1990) were adopted for training and prediction curves of the velocity distributions using the five different machine learning techniques. Three theoretical formulas were employed for comparison and investigation, the power-law derived by Takahashi (1978) based on Bagnold dilatant flow, theory modified by Matsumura and Mizuyama (1990), and the two-region formula derived by Su et al. (1993). R-squared scores for each case were calculated to check the fitness of the machine learning results to the experimental data and then to verify the fitness of the theoretical formulas to the machine learning predictions. The quantified results revealed that machine learning schemes provide powerful approaches for building prediction models for velocity distribution of debris flows.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/11124/173205
dc.identifier.urihttp://dx.doi.org/10.25676/11124/173205
dc.languageEnglish
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.publisher.originalAssociation of Environmental and Engineering Geologists
dc.relation.ispartofSeventh International Conference on Debris-Flow Hazards Mitigation - Proceedings
dc.relation.ispartofAssociation of Environmental and Engineering Geologists; special publication 28
dc.rightsCopyright of the original work is retained by the authors.
dc.sourceContained in: Proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation, Golden, Colorado, USA, June 10-13, 2019, https://hdl.handle.net/11124/173051
dc.subjectdata analysis
dc.subjectdebris flows
dc.subjectmachine learning
dc.subjectnonlinear regression
dc.subjectvelocity distribution
dc.titleOn the regression of velocity distribution of debris flows using machine learning techniques
dc.typeText


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