Classification of Annual Temperature of Iran Meteorological Stations Using Fuzzy Cluster Analysis and Kohonen Artificial Neural Networks

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agriculture, the University of Tabriz

2 MSc Student, Department of Water Engineering, Faculty of Agriculture, the University of Tabriz.

3 Department of Statistics, Faculty of Math, the University of Tabriz

4 MSc Student, Department of Water Engineering, Faculty of Agriculture the University of Tabriz

2

Abstract

Abstract
Classification of Meteorological stations, causing a large volume of data to be allocated a smaller homogeneous groups, Ease of use in modeling and also can help to spread the information to point to the lack of regional  data to Statistics regional statistic. Meteorological stations in the region has a crucial role in the management and effective use of information. In this study, 112 stations were analyzed in order to classify regions with fuzzy cluster analysis and Kohonen artificial neural Networks. Four parameters, namely mean annual temperature, longitude, latitude and elevation are considered as the classification criteria for grouping to obtain the optimal number of groups the lowest value of Davies- Bouldin index were used. Demarton climatic zonation was performed to evaluate the spatial distribution of clusters obtained from various methods. The results showed that the fuzzy clustering technique with the Demarton climatic zones is more consistent.

Keywords

Main Subjects

References

ـ رحـیمی، د.، ولی پور، ق.، یزدان پناه، ح. (1390)، «کاربـرد سامانـه و مجموعه­های فازی در پهنه­بندی دمایی (استان چهارمحال و بختیاری)»، مجله جغرافیا و برنامه­ریزی محیطی، سال 22، شماره پیاپی 41، صص 96-85.
ـ سلطانی، آ.؛ صدوقی یزدی، ه‍.؛ اشک زری طوسی، س.؛ روحانی، م. (1389)، «بهبود شبکه خودسازمانده­کوهونن با هدف خوشه­بندی داده­های فازی»، دهمین کنفرانس سیستم­های فازی ایران، دانشگاه شهید بهشتی.
ـ کوره­پزان دزفولی، ا. (1387)، «اصول تئوری مجموعه­های فازی و کاربرد­های آن در مدل­های مسائل منابع آب»، انتشارات جهاد دانشگاهی (دانشگاه صنعتی امیرکبیر).
-Dikbas F, Firat M, Koc A.cem., and Gungor M (2011), “Classification of precipitation series using fuzzy cluster method”, International Journal of Climatology, 32: 1596-1603.
-Jingyi Z and Hall M.J (2004), “Regional flood frequency analysis for the Gan-Ming river basin in China”, Journal of Hydrology, 296: 98-117.
-Holawe, F. Dutter, R. (1999), “Geostatistical study of precipitation series in Austria, Journal of Hydrology, 219: 70-82.
-Anonym, (1997), “S-PLUS4 Guides to Statistics”, Data Analysis Products Division, MathSoft, Seattle.
 -Raju, K.S, and Kumar D.N. (2007), “Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks”, Nordic Hydrology, 38(3): 303-314.
-Roger M.C, Durk R, Cazemier, P. (2000), “Representing and processing uncertain soil information for mapping soil hydrological properties”, Computers and Electronics in Agriculture, 29: 41-57.
-Ross T.J. (1995), “Fuzzy Logic with Engineering Applications”, McGraw-Hill, New York.

Files

Share

How to cite

Statistics