Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey

被引：43
作者：
Citakoglu H. [1 ]
机构：
[1] Civil Engineering Department, Engineering Faculty, Erciyes University, Kayseri
来源：
Arabian Journal of Geosciences | 2021年 / 14卷 / 20期
关键词：
Air temperature; Gaussian process regression; Long short-term memory; Support vector machine regression;
D O I：
10.1007/s12517-021-08484-3
中图分类号：
学科分类号：
摘要：
Performance of four different machine learning–based approaches (long short-term memory (LSTM), support vector machine regression (SVMR), Gaussian process regression (GPR), and multi-gene genetic programming (MGGP) models) in estimation of long-term monthly temperatures was investigated in this study. Data of 250 measuring stations of Turkey were used in present trials. Month numbers of the year, latitude, longitude, and altitude variables were used as input data of the models. Error statistics of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R2), and Nash−Sutcliffe efficiency coefficient (NSE) were used while comparing the four models. In terms of five error statistics, models yielded similar outcomes. Therefore, Taylor and Violin diagrams were used to assess how close the model-estimated values to measured data. Taylor and Violin diagrams revealed that GPR model had better performance in estimation of maximum and average temperatures than the other three models. Also, it was determined that the measured data estimated by the Kruskal–Wallis test came from the same distribution. At the end of this study, efficiency of the methods recommended for comparisons was proven. © 2021, Saudi Society for Geosciences.
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  • [51] Rehman S., Mohandes M., Artificial neural network estimation of global solar radiation using air temperature and relative humidity, Energy Policy, 36, 2, pp. 571-576, (2008)
  • [52] Sariturk B., Bayram B., Duran Z., Seker D.Z., Feature extraction from satellite images using Segnet and fully convolutional networks (FCN), Int J Eng Geosci, 5, 3, pp. 138-143, (2020)
  • [53] Sattari M.T., Apaydin H., Band S.S., Mosavi A., Prasad R., Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation, Hydrol Earth Syst Sci, 25, 2, pp. 603-618, (2021)
  • [54] Searson D.P., Leahy D.E., Willis M.J., GPTIPS: An open source genetic programming toolbox for multigene symbolic regression, Proceedings of the International Multi Conference of Engineers and Computer Sci. 1:17. IMECS, Hong Kong., (2010)
  • [55] Su H., Li X., Yang B., Wen Z., Wavelet support vector machine-based prediction model of dam deformation, Mech Syst Signal Process, 110, pp. 412-427, (2018)
  • [56] Taylor K.E., Summarizing multiple aspects of model performance in a single diagram, J Geophys Res-Atmos, 106, D7, pp. 7183-7192, (2001)
  • [57] Tikhamarine Y., Souag-Gamane D., Kisi O., A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO), Arab J Geosci, 12, 17, pp. 1-20, (2019)
  • [58] Yakut E., Suzulmus S., Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey, Netw Comput Neural Syst, 31, 1-4, pp. 1-36, (2020)
  • [59] Yin J., Deng Z., Ines A.V., Wu J., Rasu E., Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM), Agric Water Manag, 242, (2020)
  • [60] Zahroh S., Hidayat Y., Pontoh R.S., Modeling and Forecasting Daily Temperature in Bandung, (2019)
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