AIR TEMPERATURE FORECAST OF KARS CITY USING DEEP LEARNING TECHNIQUE

Abstract

Accurate estimation of air temperature plays an important role in water resource management, land-atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Deep learning techniques have been proposed to predict air temperature in recent years. This study presents a comprehensive review of long short-term memory (LSTM), one of the artificial neural networks (ANN)-based approaches used to predict air temperature. Data including weather data, average wind speed, precipitation, snowfall, snow depth, average temperature, maximum temperature and minimum temperature have been input to this algorithm. As the output, it is determined as the average temperature for the next day. The focal point is the meteorological measurements of the Central district of Kars province in the period 2010-2021. The review shows that neural network models can be used as promising tools to predict air temperature. Although ANN-based approaches are widely used for estimating air temperature due to their fast computational speed and ability to deal with complex problems, there is still no consensus on the best available method. The monthly and daily calculated model, its high estimation accuracy, showed that this model can be successfully applied in temperature estimation studies.

Keywords

• Forecasting
• Data Analysis
• Artificial Neural Networks
• LSTM

DERİN ÖĞRENME TEKNİĞİ KULLANILARAK KARS İLİNİN HAVA SICAKLIK TAHMİNİ

Abstract

Hava sıcaklığının doğru tahmini, su kaynakları yönetiminde, kara-atmosfer etkileşiminde ve tarımda önemli bir rol oynar. Ancak, doğrusal olmayan ve kaotik doğası nedeniyle hava sıcaklığını doğru bir şekilde tahmin etmek zordur. Son yıllarda hava sıcaklığını tahmin etmek için derin öğrenme teknikleri önerilmiştir. Bu çalışma, hava sıcaklığını tahmin etmek için kullanılan yapay sinir ağı (YSA) tabanlı yaklaşımlarından uzun kısa süreli bellek (LSTM) kapsamlı bir incelemesini sunmaktadır. Hava durumu verileri, ortalama rüzgâr hızı, yağış, kar yağışı, kar derinliği, ortalama sıcaklık, maksimum sıcaklık ve minimum sıcaklığı içeren veriler bu algoritmaya girdi olmuşturlar. Çıktı olarak ise, bir sonraki gün için ortalama sıcaklık olarak belirlenmiştir. Odak noktası Kars ilinin Merkez ilçesinin 2010-2021 dönemindeki meteorolojik ölçümlerdir. İnceleme, sinir ağı modellerinin hava sıcaklığını tahmin etmek için umut verici araçlar olarak kullanılabileceğini göstermektedir. YSA tabanlı yaklaşımlar, hızlı işlem kabiliyeti ve karmaşık problemlerle başa çıkma yetenekleri nedeniyle hava sıcaklığını tahmin etmek için yaygın olarak kullanılmasına rağmen, mevcut en iyi yöntem üzerinde henüz bir fikir birliği yoktur. Aylık ve günlük olarak hesaplanan modelin tahmin doğruluğunun yüksek olması, sıcaklık tahmini çalışmalarında bu modelin başarılı bir şekilde uygulanabileceğini göstermiştir.

Keywords

• Sıcaklık Tahmini
• Veri Analizi
• LSTM
• Yapay Sinir Ağları

References

1. Tajfar, E.; Bateni, S.M.; Lakshmi, V.; 2020. Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model. J. Hydrol. 583, 124577.
2. Valipour, M.; Bateni, S.M.; Gholami Sefidkouhi, M.A.; Raeini-Sarjaz, M.; Singh, V.P, 2020. Complexity of Forces Driving Trend ofReference Evapotranspiration and Signals of Climate Change. Atmosphere (Basel), 11, 1081.
3. Schulte, P.A.; Bhattacharya, A.; Butler, C.R.; Chun, H.K.; Jacklitsch, B.; Jacobs, T.; Kiefer, M.; Lincoln, J.; Pendergrass, S.; Shire, J.;et al., 2016. Advancing the framework for considering the effects of climate change on worker safety and health. J. Occup. Environ. Hyg. 13, 847–865.
4. Sardans, J.; Peñuelas, J.; Estiarte, M., 2006. Warming and drought alter soil phosphatase activity and soil P availability in a Mediterranean shrubland. Plant Soil, 289, 227–238.
5. Tang, C.; Crosby, B.T.; Wheaton, J.M.; Piechota, T.C. Assessing streamflow sensitivity to temperature increases in the Salmon River Basin, Idaho. Glob. Planet. Change 2012, 88–89, 32–44.
6. Jovic, S.; Nedeljkovic, B.; Golubovic, Z.; Kostic, N. Evolutionary algorithm for reference evapotranspiration analysis. Comput. Electron. Agric. 2018, 150, 1–4.
7. Marzo, A.; Trigo, M.; Alonso-Montesinos, J.; Martínez-Durbán, M.; López, G.; Ferrada, P.; Fuentealba, E.; Cortés, M.; Batlles, F.J., 2017. Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation. Renew. Energ, 113, 303–311.
8. Smith, D.M.; Cusack, S.; Colman, A.W.; Folland, C.K.; Harris, G.R.; Murphy, J.M. Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model. Science 2007, 317, 796–799.

9. Yang, T.; Sun, F.; Gentine, P.; Liu, W.; Wang, H.; Yin, J.; Du, M.; Liu, C., 2019. Evaluation and machine learning improvement of global hydrological model-based flood simulations. Environ. Res. Lett., 14. Water, 13, 1294 14 of 15.
10. Lee, J.; Kim, C.G.; Lee, J.E.; Kim, N.W.; Kim, H., 2018. Application of artificial neural networks to rainfall forecasting in the Geum River Basin, Korea. Water (Switzerland), 10, 1448.
11. Zou, Q.; Xiong, Q.; Li, Q.; Yi, H.; Yu, Y.; Wu, C., 2020, A water quality prediction method based on the multi-time scale bidirectional long short-term memory network. Environ. Sci. Pollut. Res., 27, 16853–16864.
12. Altan Dombayci, Ö.; Gölcü, M. Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renew. Energy 2009, 34, 1158–1161.
13. Ustaoglu, B.; Cigizoglu, H.K.; Karaca, M. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol. Appl. 2008, 15, 431–445.
14. Kumar, P.; Kashyap, P.; Ali, J. Temperature Forecasting using Artificial Neural Networks (ANN). J. Hill Agric. 2013.
15. Tran, T.T.K.; Lee, T.; Kim, J.S. Increasing neurons or deepening layers in forecasting maximum temperature time series? Atmosphere (Basel) 2020, 11, 1072.
16. Li, C.; Zhang, Y.; Zhao, G. Deep Learning with Long Short-Term Memory Networks for Air Temperature Predictions. In Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), Dublin, Ireland, 16–18 October 2019; pp. 243–249.
17. Abhishek, K.; Singh, M.P.; Ghosh, S.; Anand, A. Weather Forecasting Model using Artificial Neural Network. Procedia Technol. 2012, 4, 311–318.
18. Afzali, M.; Afzali, A.; Zahedi, G. The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction. Int. J. Environ. Sci. Dev. 2012, 3, 33–38.
19. Salman, A.G., Kanigoro, B., Heryadi, Y., 2015. Weather forecasting using deep learning techniques, in: 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE. pp. 281–285.
20. Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c., 2015. Convolutional lstm network: A machine learning approach for precipitation nowcasting, in: Advances in neural information processing systems, pp. 802–810.
21. Yang, Q., Lee, C.Y., Tippett, M.K., 2020. A long short-term memory model for global rapid intensification prediction. Weather and Forecasting 35, 1203–1220. Hochreiter, S. Long Short-Term Memory. Neural Comput. 1997, 1780, 1735–1780.
22. Salcedo-Sanz, S.; Deo, R.C.; Carro-Calvo, L.; Saavedra-Moreno, B., 2016. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor. Appl. Climatol., 125, 13–25.
23. Rajendra, P.; Murthy, K.V.N.; Subbarao, A.; Boadh, R., 2019. Use of ANN models in the prediction of meteorological data. Model. Earth Syst. Environ., 5, 1051–1058.
24. Smith, B.A.; Mcclendon, R.W., 2007. Hoogenboom, G. Improving Air Temperature Prediction with Artificial Neural Networks. Int. J. Comput. Inf. Eng,, 1, 3159.