Regional Flood Frequency Analysis by Self-Organizing Feature Maps and Fuzzy Clustering Approach

Document Type : Original Article

Authors

1 Ph.D studentStudent of, Irrigation and Drainage, Faculty of Agriculture, Ferdowsi University, Mashhad, Iran

2 Associate professorProfessor, Faculty of Natural Resources, Mountainous and Dry Region Restoration Group, Tehran University, of Tehran, Iran

Abstract

One of the methods for estimation of flood quantiles in ungauged watersheds or watersheds with short data records is using the regional frequency analysis method. In regional studies, the clustering  methods are used to achieve homogeneous regions. Self-Organization Feature Map (SOFM) is recently used in several researches for clustering the watersheds. However the interpretation of the SOFMs output units is one of the SOFMs problems.  Consequently, the trained SOFM units are used as input to the other clustering algorithms. In this study, SOFM method is used to form a two- dimension feature map, and then output nodes are fed to fuzzy c-mean clustering to form the required regions for flood frequency analysis. The optimum number of the clusters is determined by Xie-Beni and Kwon indices. The results showed that this approach has a good performance to determine homogeneous regions in Mazandaran province, northern Iran.
 

Keywords

References

فرسادنیا ف، رستمی کامرود م، مقدم­نیا ع (1391) تحلیل روند بارندگی در استان مازندران با استفاده از روش من– کندال منطقه­ای. تحقیقات منابع آب ایران، سال 8، شماره 2: 60-70.
مطالعات جامع مهندسی رودخانه‌های استان مازندران، وزارت نیرو، شرکت سهامی ‌آب منطقه‌ای استان مازندران، (1387).
Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York.
Burn DH and Goel NK (2000) The formation of groups for regional flood frequency analysis. Hydrological Sciences Journal 45(1): 97-112.
Chavoshi S, Azmin Sulaiman WN, Saghafian B, Sulaiman MD NB, Latifah AM (2012) Soft and hard clustering methods for delineation of hydrological homogeneous regions in the southern strip of the Caspian Sea Watershed. Journal of Flood Risk Management, 5(‌4): 282-294.
Greenwood JA, Landwehr JM, Matalas NC, Wallis JR (1979) Probability weighted moments: Definition and relation to parameters of several distributions expressible in inverse form. Water Resources Research. 15: 1049–1054.
Hall MJ, Minns AW (1999) The classification of hydrologically homogeneous regions. Hydrological Sciences Journal. 44 (5):  693–704.
Hathaway R J, Bezdek J C (2001) Fuzzy c-means clustering of incomplete data, IEEE Trans. Syst. Man Cybern. B. 31: 735– 744.
Haykin S (2003) Neural networks: A comprehensive foundation. Fourth Indian Reprint, Pearson Education, Singapore, p. 842.
Hosking JRM, Wallis JR (1993) Some statistics useful in regional frequency analysis. Water Resources Research. 29: 271–281.
Hosking JRM, Wallis JR (1996) Regional frequency analysis: an approach based on Lmoments. Cambridge University Press: Cambridge.
Hosking JRM (1986) The theory of probability weighted moments. Res. Rep. RC 12210, IBM Research Division, Yorktown Heights, NY.
Hosking JRM (1991) Fortran routines for use with the method of L-moments, Version 2, Res. Rep. RC 17097, IBM Research Division, York Town Heights, NY 10598.
Jingyi Z, Hall MJ (2004) Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology 296: 98–117.
Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43: 59–69.
Kohonen T (2001) Self-organizing maps. Springer, Berlin, Germany.
Kwon SH (1998) Cluster validity index for fuzzy clustering. Electronics Letters 34 (22): 2176–2177.
Lampinen J, Oja E (1992) Clustering properties of hierarchical self-organizing maps. Journal of Mathematical Imaging and Vision 2 (2–3): 261–272.
Ley R, Casper MC, Hellebrand H, Merz R (2011) Catchment classification by runoff behaviour with self-organizing maps (SOM). Hydrology and Earth System Sciences 15(9): 2947-2962.
Lin G, Chen L (2006) Identification of homogenous regions for regional frequency analysis using the self-organizing map. Journal of Hydrology 324: 1-9.
Lin G, Wang C (2006) Performing cluster analysis and discrimination analysis of hydrological factors in one step. Advances in Water Resources 29:‌1573-1585.
Rao AR, Srinivas VV (2006) Regionalization of watersheds by fuzzy cluster analysis. Journal of Hydrology 318: 57-79.
Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York.
Srinivas VV, Tripathi S, Rao AR, Govindaraju RS (2007) Regional flood frequency analysis by combining self-organizing feature map and fuzzy clustering Journal of Hydrology 348: 148– 166.
Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Frequency analysis of extreme Event, Handbook of Hydrology. McGraw-Hill: New York.
Tallaksen LM, Madsen H, Hisdal H (2004) Frequency analysis, hydrological drought – Processes and Estimation Methods for Stream flow and Groundwater, Developments in Water Sciences 48. Elsevier Science Publisher: The Netherlands.
Ultsch A, Siemon HP (1990) Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of INNC’90, International Neural Network Conference. Kluwer Academic Publishers, Dordrecht, Netherlands: 305-308.
Ultsch A (1993) Self-organizing neural networks for visualization and classification. In: Opitz, O., Lausen, B., Klar, R. (Eds.), Information and Classification. Springer, Berlin: 307-313.
Vesanto J, Alhoniemi R (2000) Clustering of the self organizing map. IEEE Trans. Neural. Netw. 11 (3): 586-600.
Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOFM Toolbox for Matlab 5. Technical Report A57. Neural Networks Research Centre, Helsinki University of Technology, Helsinki, Finland.
Vogel RM, Fennessey NM (1993) L-moment diagram should replace product moment diagram. Water Resources Research. 29: 1745–1752.
Wilppu R (1997) The visualisation capability of self organizing maps to detect of deviation in distribution control. TUCS Technical Report No. 153. Turku Centre for Computer Science, Finland.
Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Transactions on pattern analysis and machine intelligence 13 (8):  841–847.
 
 
Iran-Water Resources Research
Volume 9, Issue 3
February 2014
Pages 24-36

Files

History

  • Receive Date: 07 October 2012
  • Accept Date: 07 July 2013

Share

How to cite

Statistics