Estimation of missing temperature data by Artificial Neural Network (ANN)
Yıl: 2021 Cilt: 12 Sayı: 2 Sayfa Aralığı: 431 - 438 Metin Dili: İngilizce DOI: 10.24012/dumf.852821 İndeks Tarihi: 09-01-2022
Estimation of missing temperature data by Artificial Neural Network (ANN)
Öz: Ensuring more reliable and quality meteorological and climatological studies by providing data continuity and widening the data range. For this reason, missing values in meteorological data such as temperature, precipitation, evaporation must be completed. In this study, an artificial neural network (ANN) model was used to complete missing temperature data in the Horasan meteorology station. To establish the ANN model, monthly average temperature values of neighboring stations having similar climatic characteristics and altitude with Horasan were used as input. The monthly average temperature values of the Horasan station were used as output. Approximately 70% of the data was used for training, about 15% for testing, and about 15% for verification in the ANN model. Various statistical parameters were compared to determine the best network architecture and best model. As a result, the model's high determination coefficient (R2 = 0.99) and low mean absolute error (MAE = 0.61) showed that the ANN model can be used effectively in estimating missing temperature data.
Anahtar Kelime: -
Öz: Veri sürekliliğinin sağlanması ve aralığın genişletilmesi ile meteorolojik ve klimatolojik çalışmaların daha güvenilir ve kaliteli olmasını sağlamaktadır. Bu nedenle sıcaklık, yağış, buharlaşma gibi meteorolojik verilerde eksik olan değerlerin tamamlanması gerekmektedir. Bu çalışmada, Horasan meteoroloji istasyonundaki eksik sıcaklık verilerini tamamlamak için Yapay sinir ağı (YSA) modeli kullanılmıştır. YSA modelinin kurulması için Horasan ile benzer iklim özelliklerine ve rakıma sahip komşu istasyonların aylık ortalama sıcaklık değerleri girdi olarak kullanılmıştır. Horasan istasyonunun aylık ortalama sıcaklık değerleri ise çıkış olarak kullanılmıştır. YSA modelinde verilerin yaklaşık% 70'i eğitim için, yaklaşık% 15'i test için ve yaklaşık% 15'i doğrulama için kullanılmıştır. En iyi ağ mimarisini ve en iyi modeli belirlemek için çeşitli istatistiksel parametreler karşılaştırılmıştır. Sonuç olarak, modelin yüksek belirlilik katsayısı (R2 = 0.99) ve düşük ortalama mutlak hataya (OMH = 0.61) sahip olması YSA modelinin eksik sıcaklık verilerini tahmin etmede etkin bir şekilde kullanılabileceğini göstermiştir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- 1. Şen, Z. Artificial neural networks principles. Water Foundation, 2004.
- 2. Pielke, R.A.; Cotton, W.R.; Walko, R.E.A.; Tremback, C.J.; Lyons, W.A.; Grasso, L.D.; ... and Copeland, J.H. A comprehensive meteorological modeling system—RAMS. Meteorology and atmospheric Physics 1992, 49(1-4), 69-91.
- 3. Güç, R. Solar energy analysis and temperature forecast with artificial neural networks for bilecik province, Bilecik Şeyh Edebali University, Institute of science and technology, Bilecik, 2016.
- 4. Sanikhani, H.; Deo, R. C.; Samui, P.; Kisi, O.; Mert, C.; Mirabbasi, R.; ... & Yaseen, Z. M. Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Computers and Electronics in Agriculture 2018, 152, 242-260.
- 5. Vakili, M.; Sabbagh-Yazdi, S. R.; Khosrojerdi, S.; & Kalhor, K. Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data. Journal of cleaner production 2017, 141, 1275-1285.
- 6. Behmanesh, J; Mehdizadeh, S. Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region. Environmental Earth Sciences 2017, 76(2), 76.
- 7. Zhu, S.; Heddam, S.; Nyarko, E. K.; Hadzima-Nyarko, M.; Piccolroaz, S.; Wu, S. Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environmental Science and Pollution Research 2019, 26(1), 402-420.
- 8. Taşar, B.; Üneş, F.; Demirci, M.; Kaya, Y. Z. Evaporation amount estimation using artificial neural networks method, Dicle University Journal of Engineering 2018, vol. 9, no. 1, pp. 543-551.
- 9. Rahman, S.A.; Chakrabarty, D. Sediment transport modelling in an alluvial river with artificial neural network. Journal of Hydrology 2020, 588, 125056.
- 10. Yıldıran A.; Kandemir, S.Y. Estimation of Rainfall Amount with Artificial Neural Networks, Bilecik Şeyh Edebali University Journal of Science 2018, vol. 5, no. 2, pp. 97-104.
- 11. Afzaal, H.; Farooque, A. A.; Abbas, F.; Acharya, B.; Esau, T. Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water 2020, 12(1), 5.
- 12. Dalkiliç, H. Y.; Hashimi, S. A. Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models. Water Supply 2020, 20(4), 1396-1408.
- 13. Kızılaslan, M.; Sağın, F.; Doğan, E.; Sönmez, O. Estimation of lower Sakarya River flow using artificial neural networks," Sakarya University Journal of the Institute of Science 2014 vol. 18, no. 2, pp. 99-103.
- 14. Minns, A.; Hall, M. Artificial neural networks as rainfall-runoff models, Hydrological sciences journal 1996, vol. 41, no. 3, pp. 399-417.
- 15. Bishop, C.M. Neural networks and their applications, Review of scientific instruments 1994, vol. 65, no. 6, pp. 1803-1832.
- 16. Campolo, M.; Andreussi, P; Soldati, A. River flood forecasting with a neural network model, Water resources research 1999 vol. 35, no. 4, pp. 1191-1197.
- 17. Ilie, C.; Ilie, M.; Melnic, L.; Topalu, A.-M. Estimating the Romanian Economic Sentiment Indicator Using Artificial Intelligence Techniques. Journal of Eastern Europe Research in Business & Economics 2012, 1.
- 18. Hocking, R.R. A Biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics, 1976, 32(1), 1-49.
- 19. Lindley, D.V. Regression and correlation analysis. In Time Series and Statistics Palgrave Macmillan, London. 1990; pp. 237-243.
- 20. Dombaycı, Ö. A.; and Gölcü, M. Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy, 2009, 34(4), 1158-1161.
- 21. Akyüz, A. Ö.; Kumaş, K.; Ayan, M,; Güngör, A. Antalya İli Meteorolojik Verileri Yardımıyla Hava Sıcaklığının Yapay Sinir Ağları Metodu ile Tahmini. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2020, 10(1), 146-154.
| APA | Katipoğlu O, ACAR R (2021). Estimation of missing temperature data by Artificial Neural Network (ANN). , 431 - 438. 10.24012/dumf.852821 |
| Chicago | Katipoğlu Okan Mert,ACAR RESAT Estimation of missing temperature data by Artificial Neural Network (ANN). (2021): 431 - 438. 10.24012/dumf.852821 |
| MLA | Katipoğlu Okan Mert,ACAR RESAT Estimation of missing temperature data by Artificial Neural Network (ANN). , 2021, ss.431 - 438. 10.24012/dumf.852821 |
| AMA | Katipoğlu O,ACAR R Estimation of missing temperature data by Artificial Neural Network (ANN). . 2021; 431 - 438. 10.24012/dumf.852821 |
| Vancouver | Katipoğlu O,ACAR R Estimation of missing temperature data by Artificial Neural Network (ANN). . 2021; 431 - 438. 10.24012/dumf.852821 |
| IEEE | Katipoğlu O,ACAR R "Estimation of missing temperature data by Artificial Neural Network (ANN)." , ss.431 - 438, 2021. 10.24012/dumf.852821 |
| ISNAD | Katipoğlu, Okan Mert - ACAR, RESAT. "Estimation of missing temperature data by Artificial Neural Network (ANN)". (2021), 431-438. https://doi.org/10.24012/dumf.852821 |
| APA | Katipoğlu O, ACAR R (2021). Estimation of missing temperature data by Artificial Neural Network (ANN). Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(2), 431 - 438. 10.24012/dumf.852821 |
| Chicago | Katipoğlu Okan Mert,ACAR RESAT Estimation of missing temperature data by Artificial Neural Network (ANN). Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 12, no.2 (2021): 431 - 438. 10.24012/dumf.852821 |
| MLA | Katipoğlu Okan Mert,ACAR RESAT Estimation of missing temperature data by Artificial Neural Network (ANN). Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol.12, no.2, 2021, ss.431 - 438. 10.24012/dumf.852821 |
| AMA | Katipoğlu O,ACAR R Estimation of missing temperature data by Artificial Neural Network (ANN). Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi. 2021; 12(2): 431 - 438. 10.24012/dumf.852821 |
| Vancouver | Katipoğlu O,ACAR R Estimation of missing temperature data by Artificial Neural Network (ANN). Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi. 2021; 12(2): 431 - 438. 10.24012/dumf.852821 |
| IEEE | Katipoğlu O,ACAR R "Estimation of missing temperature data by Artificial Neural Network (ANN)." Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12, ss.431 - 438, 2021. 10.24012/dumf.852821 |
| ISNAD | Katipoğlu, Okan Mert - ACAR, RESAT. "Estimation of missing temperature data by Artificial Neural Network (ANN)". Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 12/2 (2021), 431-438. https://doi.org/10.24012/dumf.852821 |
