A Modified Radial Basis Function Method for Predicting Debris Flow Mean Velocity

Wenmin Yang

Abstract


This study focused on a model for predicting debris flow mean velocity. A total of 50 debris flow events were investigated in the Jiangjia gully. A modified radial basis function (MRBF) neural network was developed for predicting the debris flow mean velocity in the Jiangjia gully. A three-dimensional total error surface was used for establishing the predicting model. A back propagation (BP) neural network and the modified Manning formula (MMF) were used as benchmarks. Finally, the sensitivity degrees of five variables that influence debris flow velocity were analyzed. The results show that the mean error and the relative mean error of the 10 testing samples were only 0.31 m/s and 5.92%, respectively. This proves that the MRBF method performed very well in predicting debris flow mean velocity. Gradient of channel and unstable layer thickness have a greater impact on debris flow mean velocity than the other three influencing variables. This proves that the proposed MRBF neural network is reliable in predicting debris flow mean velocity.

Keywords


debris flow; disaster risk reduction; mean velocity; radial basis function; sensitive variables sequence.

Full Text:

PDF

References


Cao, C., Xu, P., Chen, J., Zheng, L. & Niu, C., Hazard Assessment of Debris-Flow Along The Baicha River in Heshigten Banner, Inner Mongolia, China, International Journal of Environmental Research and Public Health, 14(1), pp. 30, 2016.

Guzzetti, F., Stark, C.P. & Salvati, P., Evaluation of Flood and Landslide Risk To The Population Of Italy, Environ Manage, 36(1), pp. 15-36. 2005.

Hilker, N., Badoux, A. & Hegg, C., The Swiss Flood and Landslide Damage Database 1972-2007, Natural Hazards and Earth System Sciences, 9(3), pp. 913-925, 2009.

Tang, C., Zhu, J., Li, W.L. & Liang, J.T., Rainfall-Triggered Debris Flows Following The Wenchuan Earthquake, B. Eng. Geol. Environ., 68(2), pp. 187-194, 2009.

Cui, P., Zhu, Y.Y., Han, Y.S., Chen, X.Q. & Zhuang, J.Q., The 12 May Wenchuan Earthquake-Induced Landslide Lakes: Distribution and Preliminary Risk Evaluation, Landslides, 6(3), pp. 209-223, 2009.

Cui, P., Chen, X.Q., Zhu, Y.Y., Su, F.H., Wei, F.Q., Han, Y.S., Liu, H.J. & Zhuang, J.Q., The Wenchuan Earthquake (May 12, 2008), Sichuan Province, China, and Resulting Geohazards, Natural Hazards, 56(1), pp. 19-36, 2011.

Li, Y., Liu, J.J., Hu, K.H. & Su, P.C., Probability Distribution of Measured Debris-Flow Velocity in Jiangjia Gully, Yunnan Province, China, Natural Hazards, 60(2), pp. 689-701, 2012.

Takahashi, T., Debris Flow: Mechanics, Prediction and Countermeasures, CRC press, 2014.

Chen, C.L., Generalized Viscoplastic Modeling of Debris Flow, Journal of Hydraulic Engineering, 114(3), pp. 237-258, 1988.

Iverson, R.M., Reid, M.E. & LaHusen, R.G., Debris-Flow Mobilization from Landslides, Annual Review of Earth and Planetary Sciences, 25, pp. 85-138, 1997.

Benda, L.E. & Cundy, T.W., Predicting Deposition of Debris Flows in Mountain Channels, Canadian Geotechnical Journal, 27(4), pp. 409-417, 1990.

Bathurst, J., Burton, A. & Ward, T.J., Debris Flow Run-Out and Landslide Sediment Delivery Model Tests, Journal of Hydraulic Engineering, 123(5), pp. 410-419, 1997.

Arattano, M., Marchi, L. & Cavalli, M., Analysis of Debris-Flow Recordings in An Instrumented Basin: Confirmations and New Findings, Natural Hazards and Earth System Sciences, 12, pp. 679-686, 2012.

Yuan, L., Li, W., Zhang, Q. & Zou, L., In Debris Flow Hazard Assessment Based on Support Vector Machine, Geoscience and Remote Sensing Symposium, IEEE International Conference on IEEE, pp. 4221-4224, 2006.

Chang, T.C., Risk Degree of Debris Flow Applying Neural Networks, Natural Hazards, 42(1), pp. 209-224, 2007.

Liu, G., Dai, E., Ge, Q., Wu, W. & Xu, X., A Similarity-Based Quantitative Model for Assessing Regional Debris-Flow Hazard, Natural hazards, 69(1), pp. 295-310, 2013.

Chen, J., He, Y. & Wei, F., Debris Flow Erosion and Deposition in Jiangjia Gully, Yunnan, China, Environ. Geol., 48(6), pp. 771-777, 2005.

Cui, P., Chen, X., Wang, Y., Hu, K. & Li, Y., Jiangjia Ravine Debris Flows in South-Western China, In Debris-flow Hazards and Related Phenomena, Springer, pp. 565-594, 2005.

Li, J., Yuan, J., Bi, C. & Luo, D., The Main Features of the Mudflow in Jiang-Jia Ravine, Z. Geomorphol, 27, pp. 325-341, 1983.

Xu, Y., Study on Flow Mechanism for Avalanche Soils and Scour and Silting Characteristics of Debris Flows, Beijing: China Institute of Water Conservancy, 2001.

Zhang, Y., Lu, Y. & Li, W., RBF Network of Hidden Layer Nodes Optimization, Computer Technology and Development, 1, pp. 103-105, 2009.

Wu, C. & Fan J., Maximal Matrix Element Method for Determining The Number of Hidden Nodes of RBF Neural Networks, Computer Engineering and Applications, 20, pp. 77-79, 2004.

Yu, G., Zhang, M. & Wang, G., Application and Comparison of Prediction Models of Support Vector Machines and Back-Propagation Artificial Neural Network For Debris Flow Average Velocity, Journal of Hydraulic Engineering, 43, pp. 105-110, 2012.

Xu, L., Wang, Q. & Chen, J., Forecast for Average Velocity of Debris Flow Based on BP Neural Network, Journal of Jilin University (Earth Science Edition), 43, pp. 186-191, 2013.

Chen, S., Cowan, C.F. & Grant, P.M., Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks, IEEE Transactions on neural networks, 2(2), pp. 302-309, 1991.

Poggio, T. & Girosi, F., Networks for Approximation and Learning, Proceedings of the IEEE, 78(9), pp. 1481-1497, 1990.

Zhi, H., Niu, K., Tian, L. & Yang, Z., A Comparative Study on Bp Network and Rbf Network in Function Approximation, Bulletin of Science and Technology, 2, pp. 193-197, 2005.




DOI: http://dx.doi.org/10.5614%2Fj.eng.technol.sci.2017.49.5.1

Refbacks

  • There are currently no refbacks.