AN INTELLIGENT INFORMATION SYSTEM FOR FUZZY ADDITIVE MODELLING (HYDROLOGICAL RISK APPLICATION)

Document Type: Research Paper

Authors

1 Department of Forestry, Management of the Environment & Natural Resources, Democritus University of Thrace, 193 Padazidou st., 68200, N. Orestiada, Greece

2 Department of Production Engineering & Management, School of Engineering, Democritus University of Thrace, University Library Building, 67100Xanthi, Greece

Abstract

In this paper we propose and construct Fuzzy Algebraic Additive
Model, for the estimation of risk in various fields of human activities or nature’s
behavior. Though the proposed model is useful in a wide range of scientific
fields, it was designed for to torrential risk evaluation in the area of river Evros.
Clearly the model’s performance improves when the number of parameters
and the actual data increases. A Fuzzy Decision Support System was designed
and implemented to incorporate the model’s risk estimation capacity and the
risk estimation output of the system was compared with the output of other
existing methods with very interesting results.

Keywords


[1] E. Cox, Fuzzy modeling and genetic algorithms for data mining and exploration, Elsevier

Morgan Kaufmann Publishers, USA, 2005.

[2] C. J. Date, An introduction to database systems, Addison-Wesley, New York, 1990.

[3] J. De Vente and J. Poesen, Predicting soil erosion and sediment yield at the basin scale:

scale issues and semi-quantitative models , Earth Science Reviews, Elsevier Science, 71(1-2) (2005), 95-125.

[4] I. Douglas, Predicting road erosion rates in selectively logged tropical rain forests, Erosion

Prediction in Ungauged Basins: Integrating Methods and Techniques (Procecdinss of symposium

I-IS01 held during IUGG2003 at Sapporo. July 2003). IAHS Ptibl, 279 (2003).

[5] S. Gavrilovic, Engineering of torrents flows and erosion, Special edition, Belgrade, 1972.

[6] Z. Gavrilovic, The use of an empirical method (Erosion potential method for calculating

sediment production and transportation in unstudied or torrential streams) , International

Conference on River Regime: Wallingford, England, 1998.

[7] L. Iliadis, F. Maris and D. Marinos, A decision support system using fuzzy relations for the

estimation of long-term torrential risk of mountainous watersheds: the case of river evros

, Proceeding ICNAAM 2004 Conference, Chalkis, Greece, 2004.

[8] M. T. Jones,AI application programming, Thomson Delmar Learning, 2nd Edition, Boston,2005.

[9] A. Kandel,Fuzzy expert systems, CRC Press Florida, USA, 1992.

[10] V. Kecman,Learning and soft computing, MIT Press. London England, 2001.

[11] B. Kosko,Global stability of generalized additive fuzzy systems, IEEE Transactions on Systems,

Man and Cybernetics – Part C: Applications and Reviews,28(3) (1998).

[12] B. Kosko,Neural networks and fuzzy systems: a dynamical systems approach to machine

learning intelligence, Englewood Cliff, NJ: Prentice-Hall, 1991.

[13] D. Kotoulas,Management of torrents I, Publications of the University of Thessaloniki, 1997.

[14] E. G. Mansoori, M. J. Zolghadri, S. D. Katebi and H. Mohabatkar,Generating fuzzy rules

for protein classification, Iranian Journal of Fuzzy Systems, 5(2) (2008).

[15] F. Maris and L. Iliadis,A computer system using two membership functions and T-norms

for the calculation of mountainous watersheds torrential risk: the case of lakes trixonida and

 

lisimaxia, Book Series: Developments in Plant and Soil Sciences, Book Title: Eco-and Ground

 

Bio-Engineering: The Use of Vegetation to Improve Slope Stability, Springer Netherlands,103(2007), 247-254.

[16] M. Meidani, G. Habibaghai and S. Katebi,An aggregated fuzzy reliability index for slope

stability analysis, Iranian Journal of Fuzzy Systems, 1(1) (2004), 17.

[17] R. Satur , Z. Liu and M. Gahegan,Multi-layered FCM’s applied to context dependent learning,

Proc. IEEE FUZZ-95,2 (1992), 561-568.

[18] A. K. Shaymal and M. Pal,Triangular fuzzy matrices, Iranian Journal of Fuzzy Systems,4(1)(2007).

[19] N. Skermer and D. Van Dine,Debris-flow hazards and related phenomena, Springer Berlin,

(2005), 25-51.

 

[20] P. Stefanidis,The torrent problems in mediterranean areas (example from greece), Proc.

XXIUFRO Congress. Finland, 1995.

 

[21] M. Sugeno and G. T. Kang,Structure identification of fuzzy model, Fuzzy Sets and Systems,

28(1988), 15-33.

[22] T. Takagi and M. Sugeno,Fuzzy identification of systems and its applications to modeling

and control, IEEE Trans. Syst. Man. Cybern., 15 (1985), 116-132.

[23] A. Tazioli,Evaluation of erosion in equipped basins: preliminary results of a comparison

between the gavrilovic model and direct measurements of sediment transport

, Environmental Geology, Springer Berlin,56(5) (2009), 825-831.

[24] R. R. Yager, S. Ovchinnikov, R. M. Tong and H. T. Nguyen,Fuzzy sets and applications:

selected papers, Wiley New York, 1987.

[25] L. A. Zadeh, Fuzzy sets, Information Control, 88 (1965), 338-353.

[26] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning,

Part I: Inf. Sci., 8, 199, Part II: Inf. Sci., 8, 301; Part II: Inf. Sci., 9, 43, 1975.

 

[27] H. J. Zimmermann, Fuzzy set theory and its applications, 2nd Edition. Boston: Kluwer, 1991.