Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 7 5 2007 10.5194/nhess-7-557-2007 http://www.nat-hazards-earth-syst-sci.net/7/557/2007/ http://www.nat-hazards-earth-syst-sci.net/7/557/2007/nhess-7-557-2007.html http://www.nat-hazards-earth-syst-sci.net/7/557/2007/nhess-7-557-2007.pdf 557 570 2007-10-09 An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey) M. C. Tunusluoglu C. Gokceoglu cgokce@hacettepe.edu.tr H. Sonmez H. A. Nefeslioglu Hacettepe University, Department of Geological Engineering, Applied Geology Division, 06532 Beytepe, Ankara, Turkey General Directorate of Mineral Research and Exploration, Department of Geological Research, Remote Sensing Center, 06520 Ankara, Turkey Various statistical, mathematical and artificial intelligence techniques have been used in the areas of engineering geology, rock engineering and geomorphology for many years. However, among the techniques, artificial neural networks are relatively new approach used in engineering geology in particular. The attractiveness of ANN for the engineering geological problems comes from the information processing characteristics of the system, such as non-linearity, high parallelism, robustness, fault and failure tolerance, learning, ability to handle imprecise and fuzzy information, and their capability to generalize. For this reason, the purposes of the present study are to perform an application of ANN to a engineering geology problem having a very large database and to introduce a new approach to accelerate convergence. For these purposes, an ANN architecture having 5 neurons in one hidden layer was constructed. During the training stages, total 40 000 training cycles were performed and the minimum RMSE values were obtained at approximately 10 000th cycle. At this cycle, the obtained minimum RMSE value is 0.22 for the second training set, while that of value is calculated as 0.064 again for the second test set. Using the trained ANN model at 10 000th cycle for the second random sampling, the debris source area susceptibility map was produced and adjusted. Finally, a potential debris source susceptibility map for the study area was produced. When considering the field observations and existing inventory map, the produced map has a high prediction capacity and it can be used when assessing debris flow hazard mitigation efforts. Akgun, A. and Bulut, F.: GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region, Environmental Geology, 51, 1377–1387, 2007. ASCE: Artificial neural networks in hydrology: I. Preliminary concepts. J. Hydrol. Eng., 5, 115–123, 2000. Basheer, I. A. and Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Methods, 43, 3–31, 2000. Can, T., Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., and Duman, T. Y.: Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three subcatchments by logistic regression analyses, Geomorphology, 72, 250–271, 2005. Chien-Yuan, C., Tien-Chien, C., Fan-Chieh, Y., Wen-Hui, Y., and Chun-Chieh, T.: Rainfall duration and debris flow initiated studies for real-time monitoring, Environmental Geology, 47, 715–724, 2005. Crowley, J. K., Hubbard, B. E., and Mars, J. C.: Analysis of potential debris flow source areas on Mount Shasta, California, bu using airborne and satellite remote sensing data, Rem. Sens. Environ., 87, 345–358, 2003. Duman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A., and Sonmez, H.: Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey, Environ. Geology, 51, 241–256, 2006. Ermini, L., Catani, F., and Casagli, N.: Artificial Neural Networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005. Fahlman, S. E.: An empirical study of learning speed in backpropagation, Technical Report CMU-CS-88-162, Carnegie-Mellon University, 1988. Fu, F.: Neural Networks in Computer Intelligence, McGraw-Hill, New York, 460 pp., 1995. Gallahger, M. and Downs, T.: Visualisation of learning in neural networks using principal component analysis, in: Proc of Int Conf on Computational Intelligence and Multimedia Applications, edited by: Varma, B. and Yao, X., Australia, 327–331, 1997. Garcin, M., Poisson, B., and Pouget, R.: High rates of geomorphological processes in a tropical area: Remparts River case study (Reunion Island, Indian Ocean), Geomorphology, 67, 335–350, 2005. Glade, T.: Linking debris-flow hazard assessments with geomorphology, Geomorphology, 66, 189–213, 2005. Gokceoglu, C. and Aksoy, H.: Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques, Eng. Geol., 44, 147–161, 1996. Gokceoglu, C., Sonmez, H., Nefeslioglu, H. A., Duman, T. Y., and Can, T.: The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity, Eng. Geology, 81, 65–83, 2005. Gomez, H. and Kavzoglu, T.: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Eng. Geology, 7, 11–27, 2005. Hassoun, M. H.: Fundamentals of Artificial Neural Networks, MIT Press, Cambridge MA, USA, 537 pp., 1995. Hecht-Nielsen, R.: Kolmogorov's mapping neural network existence theorem. Poc of the First IEEE International Conference on Neural Networks, San Diego CA, USA, 11–14, 1987. Henseler, J.: Backpropagation, in: Artificial neural networks, an introduction to ANN theory and practice, edited by: Braspenning, P. J., Lecture Notes in Computer Science, Berlin, Springer, 37–66, 1995. Hertz, J., Krogh, A., and Palmer, R. G.: Introduction to the Theory of Neural Computation, Addison-Wesley, Reading MA, 1991. Ishikawa, Y., Kawakami, S., Morimoto, C., and Mizuhara, K.: Suppression of debris movement by forests and damage to forests by debris deposition, J. Forest Res., 8, 37–47, 2003. Jain, A. K., Mao, J., and Mohiddin, K. M.: Artificial neural networks: a tutorial, Comput. IEEE March, 31–44, 1996. Jakob, M.: A size classification for debris flows, Eng. Geology, 79, 151–161, 2005. Jomelli, V., Pech, V. P., Chochillon, C., and Brunstein, D.: Geomorphic variations of debris flows and recent climatic change in the French Alps, Clim. Change, 64, 77–102, 2004. Kaastra, I. and Boyd, M.: Designing a neural network for forecasting financial and economic time series, Neurocomputing, 10(3), 215–236, 1996. Kavzoglu, T.: An investigation of the design and use of feed-forward artificial neural Networks in classification of remotely sensed images, PhD Thesis, The University of Nottingham, UK (unpublished), 2001. Klimasauskas, C. C.: Applying neural networks, in: Neural Networks in Finance and Investigating, edited by: Trippi, R. R. and Turban, E., Probus, Cambridge, 1993. Lee, S. and Evangelista, D. G.: Earthquake-induced landslide-susceptibility mapping using an artificial neural network, Nat. Hazards Earth Syst. Sci., 6, 687–695, 2006. Lee, S., Ryu, J. H., Lee, M. J., and Won, J. S.: Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea, Environ. Geology, 4, 820–833, 2003a. Lee, S., Ryu, J. H., Min, K., and Won, J. S.: Landslide susceptibility analysis using GIS and artificial neural network, Earth Surf. Processes Landf., 28, 1361–1376, 2003b. Lee, S., Ryu, J. H., Won, J. S., and Park, H. J.: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289–302, 2004. Lee, S., Ryu, J. H., Lee, M. J., and Won, J. S.: The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea, Math. Geology, 38(2), 199–220, 2006. Looney, C. G.: Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes, IEEE Transactions on Knowledge and Data Engineering, 8(2), 211–226, 1996. Looney, C. G.: Pattern Recognition Using Neural Networks. Theory and Algorithms for Engineers and Scientists, Oxford University Press, New York, 458 pp., 1997. Malet, J. P., Maquaire, O., Locat, J., and Remaitre, A.: Assessing debris flow hazards associated with slow moving landslide: methodology and numerical analyses, Landslides, 1, 83–90, 2004. Malet, J. P., Laigle, D., Remaitre, A., and Maquaire, O.: Triggering conditions and mobility of debris flows associated to complex earthflows, Geomorphology, 66, 215–235, 2005. May, C. L. and Gresswell, R. E.: Spatial and temporal patterns of debris-flow deposition in the Oregon Coast Range, USA, Geomorphology, 57, 135–149, 2004. Messer, K. and Kittler, J.: Choosing an optimal neural network size to aid search through a large image database. Proc of 9th British Machine Vision Confrence (BMVC98), Univ of Southamton, UK, 235–244, 1998. Meulenkamp, F. and Alvarez Grima, M.: Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness, Int. J. Rock Mechanics Mining Sci., 36(1), 29–39, 1999. Moore, I. D. and Burch, G. J.: Sediment transport capacity of sheet and rill flow: application of unit stream power theory, Water Resour. Res., 22, 1350–1360, 1986. Moore, I. D. and Wilson, J. P.: Length-slope factors for the Revised Universal Soil Loss Equation: simplified method for estimation, J. Soil Water Conservation, 47, 423–428, 1992. Moore, I. D., Grayson, R. B., and Ladson, A. R.: Digital terrain modeling: a review of hydrological, geomorphological, and biological applications, Hydrol. Processes, 5, 3–30, 1991. Neaupane, K. M. and Achet, S. H.: Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya, Eng. Geology, 74, 213–236, 2004. Nelson, M. and Illingworth, W. T.: A Practical Guide to Neural Nets, Addisin-Wesley, Reading, MA, 352 pp., 1990. Nemec, W. and Kazanci, N.: Quaternary colluvium in west-central Anatolia: sedimentary facies and paleoclimatic significance, Sedimentology, 46, 139–170, 1999. Negnevitsky, M.: Artificial Inteligence: A Guide to Intelligent systems, Addison-Wesley, England, 394 pp., 2002. Ozgul, N., Bolukbasi, S., Alkan, H., Oztas, Y., and Korucu, M.: Goller Bolgesinin Tektono-Stratigrafik Birlikleri, Ozan Sungurlu Sempozyumu Bildirileri, Ankara, p. 213–237 (in Turkish), 1991. Paola, J. D.: Neural network classification of multispectral imagery, MSc Thesis, The University of Arizona, USA, 1994. Rickenmann, D.: Empirical relationships for debris flows, Nat. Hazards, 19, 47–77, 1999. Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning internal representation by error propogation, in: Parallel Distributed Processing, edited by: Rumelhart, D. E. and McClelland, J. L., 1, 318–362, 1986. Sarangi, A. and Bhattacharya, A. K.: Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India, Agric. Water Manag., 78(3), 195–208, 2005. Scally, F. A. and Owens, I. F.: Depositional processes and particle characteristics on fans in the Southers Alps, New Zealand, Geomorphology, 69, 46–56, 2004. Schalkoff, R. J.: Artificial Neural Network, McGraw-Hill, New York, 448 pp., 1997. Sietsma, J. and Dow, R. J. F.: Creating artificial neural network that generalize, Neural Networks, 4, 67–69, 1991. Singh, V. K., Singh, D., and Singh, T. N.: Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks, Int. J. Rock Mechanics Mining Sci., 38(2), 269–284, 2001. Sonmez, H., Gokceoglu, C., Nefeslioglu, H. A., and Kayabasi, A.: Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation, Int. J. Rock Mechanics Mining Sci., 43(2), 224–235, 2006. Staufer, P. and Fisher, M. M.: Specral pattern recognition by a two-layer perceptron: effects of training set size, in: Neuro-Computation in Remote Sensing Data Analysis, edited by: Kanellopoulos, I., Wilkinson, G. G., Roli, F., and Austin, J., Springer, London, 105–116, 1997. Swingler, K.: Applying Neural Networks: A Practical Guide, Academic Press, New York, on CD, 1996. Tunusluoglu, M. C., Gokceoglu, C., Nefeslioglu, H. A., and Sonmez, H.: Extraction of debris source areas by logistic regression technique: A case study from Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey), Environ. Geology, in press, doi:10.1007/s00254-007-0788-5, 2007. Wen, B. P. and Aydin, A.: Mechanism of a rainfall-induced slide-debris flow: constrains from microstructure of its slip zone, Eng. Geology, 78, 69–88, 2005. Wilson, J. P. and Gallant, J. C.: Terrain analysis principles and applications, John Wiley and Sons, Inc., Canada, 479 p., 2000. Wyhthoff, B. J.: Backpropagation neural networks: a tutorial, Chemometr. Intell. Lab. Syst., 18, 115–155, 1993. Yalcin, A.: GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations, CATENA, in press, doi:10.1016/j.catena.2007.01.003, 2007. Yesilnacar, E. and Topal, T.: Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Eng. Geology, 79, 251–266, 2005. Zhang, B. and Govindaraju, R. S.: Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds, J. Hydrol., 273(1–4), 18–34, 2003.