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 50 条
[21]   Modelling long-term monthly temperatures by several data-driven methods using geographical inputs [J].
Kisi, Ozgur ;
Sanikhani, Hadi .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2015, 35 (13) :3834-3846
[22]   Long-Term Prediction of Atmospheric Corrosion Loss in Various Field Environments [J].
Cai, Yi-kun ;
Zhao, Yu ;
Ma, Xiao-bing ;
Zhou, Kun ;
Wang, Hao .
CORROSION, 2018, 74 (06) :669-682
[23]   Prediction of long-term strength of concrete based on artificial neural network [J].
Yang Xiaoming ;
Shi Dan .
ARCHITECTURE, BUILDING MATERIALS AND ENGINEERING MANAGEMENT, PTS 1-4, 2013, 357-360 :905-908
[24]   PREDICTION OF LONG-TERM STRENGTH OF CONCRETE BASED ON ARTIFICIAL NEURAL NETWORK [J].
Yang, Xiaoming ;
Shi, Dan .
PROCEEDINGS OF THE TWELFTH INTERNATIONAL SYMPOSIUM ON STRUCTURAL ENGINEERING, VOLS I AND II, 2012, :211-215
[25]   PREDICTION OF LONG-TERM STRENGTH OF CONCRETE BASED ON ARTIFICIAL NEURAL NETWORK [J].
Yang, Xiaoming ;
Shi, Dan .
FUNDAMENTAL RESEARCH IN STRUCTURAL ENGINEERING: RETROSPECTIVE AND PROSPECTIVE, VOLS 1 AND 2, 2016, :1688-1692
[26]   Prediction of daily and monthly rainfall using a backpropagation neural network [J].
Huu Nam Nguyen ;
Thuy-Anh Nguyen ;
Hai-Bang Ly ;
Van Quan Tran ;
Long Khanh Nguyen ;
Minh Viet Nguyen ;
Canh Tung Ngo .
JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2021, 24 (03) :367-379
[27]   An Integrated Intelligent Technique for Monthly Rainfall Time Series Prediction [J].
Kajornrit, Jesada ;
Wong, Kok Wai ;
Fung, Chun Che ;
Ong, Yew Soon .
2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2014, :1632-1639
[28]   Prediction of high-temperature long-term strength for damaged structures [J].
Aleksyuk M.M. .
Strength of Materials, 2002, 34 (06) :617-622
[29]   Long-term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability [J].
Abreu, Marcel Carvalho ;
de Souza Fraga, Micael ;
Lyra, Gustavo Bastos ;
de Oliveira Junior, Jose Francisco ;
Villar-Hernandez, Bartolo de Jesus ;
de Souza, Amaury ;
Zeri, Marcelo .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2023, 43 (16) :7463-7480
[30]   Prediction of long-term monthly precipitation using several soft computing methods without climatic data [J].
Kisi, Ozgur ;
Sanikhani, Hadi .
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2015, 35 (14) :4139-4150