Prediction of Long-term Monthly Temperature and Rainfall in Turkey

被引:38
作者
Bilgil, M. [1 ]
Sahin, B. [1 ]
机构
[1] Cukurova Univ, Fac Engn & Architecture, Dept Mech Engn, TR-01330 Adana, Turkey
关键词
artificial neural network; prediction; rainfall; temperature; ARTIFICIAL NEURAL-NETWORK;
D O I
10.1080/15567030802467522
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
In this study, artificial neural networks were applied to predict the long-term monthly temperature and rainfall at any target point of Turkey based on the use of the neighboring measuring stations data. For this purpose, meteorological data measured by the Turkish State Meteorological Service between the years 1975 and 2006 from 76 measuring stations were used a straining (59 stations) and testing (17 stations) data. Four neurons which receive input signals of latitude, longitude, altitude, and month were used in the input layer of the network. Two neurons, which produce corresponding out put signals of the long-term monthly temperature and rainfall, were utilized in the output layer of the network. Finally, the values determined by the artificial neural network model were compared with the actual data. Errors obtained in this model are well with in acceptable limits.
引用
收藏
页码:60 / 71
页数:12
相关论文
共 16 条
[1]   Application of artificial neural networks for the wind speed prediction of target station using reference stations data [J].
Bilgili, Mehmet ;
Sahin, Besir ;
Yasar, Abdulkadir .
RENEWABLE ENERGY, 2007, 32 (14) :2350-2360
[2]   New outdoor heating design data for Turkey [J].
Bulut, H ;
Büyükalaca, O ;
Yilmaz, T .
ENERGY, 2003, 28 (12) :1133-1150
[3]   Cross-correlations between weather variables in Australia [J].
Guan, L. ;
Yang, J. ;
Bell, J. M. .
BUILDING AND ENVIRONMENT, 2007, 42 (03) :1054-1070
[4]  
Haykin S. S., 1994, Neural Networks: A Comprehensive Foundation
[5]  
Kalogirou S.A., 1999, EUR S INT TECHN ESIT
[6]   Artificial neural networks in renewable energy systems applications: a review [J].
Kalogirou, SA .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2001, 5 (04) :373-401
[7]   Weather analysis using ensemble of connectionist learning paradigms [J].
Maqsood, Imran ;
Abraham, Ajith .
APPLIED SOFT COMPUTING, 2007, 7 (03) :995-1004
[8]   Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada [J].
Maqsood, M ;
Khan, MR ;
Huang, GH ;
Abdalla, R .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2005, 18 (01) :115-125
[9]   Artificial neural network application for multi-ecosystem carbon flux simulation [J].
Melesse, AM ;
Hanley, RS .
ECOLOGICAL MODELLING, 2005, 189 (3-4) :305-314
[10]   Artificial neural network approach to spatial estimation of wind velocity data [J].
Öztopal, A .
ENERGY CONVERSION AND MANAGEMENT, 2006, 47 (04) :395-406