2093Accesses
Abstract
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.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.







Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of data and material
Climatic data and hydrometric data provided by General Directorate of State Meteorological Affairs (DMİ) and General Directorate of State Hydralics Works (DSİ).
Code availability
The codes were written by Hatice Çıtakoğlu. And the analysis was made by Hatice Çıtakoğlu. Licensed MATLAB provided by Erciyes University.
References
Abdel-Aal RE (2004) Hourly temperature forecasting using abductive networks. Eng Appl Artif Intell 17(5):543–556
Al-Mosawe A, Kalfat R, Al-Mahaidi R (2017) Strength of Cfrp-steel double strap joints under impact loads using genetic programming. Compos Struct 160:1205–1211
Arslan N, Sekertekin A (2019) Application of long short-term memory neural network model for the reconstruction of MODIS Land Surface Temperature images. J Atmos Sol Terr Phys 194:105100
Bandyopadhyay A, Bhadra A, Raghuwanshi NS, Singh R (2008) Estimation of monthly solar radiation from measured air temperature extremes. Agric For Meteorol 148(11):1707–1718
Bartos I, Jánosi IM (2006) Nonlinear correlations of daily temperature records over land. Nonlinear Process Geophys 13(5):571–576
Benbahria Z, Sebari I, Hajji H, Smiej MF (2021) Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning. Int J Eng Geosci 6(1):41–51
Bilgili M, Sahin B (2009) Prediction of long-term monthly temperature and rainfall in Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 32(1):60–71
Chen X, Huang J, Han Z, Gao H, Liu M, Li Z, Liu X, Li Q, Qi H, Huang Y (2020) The importance of short lag-time in the runoff forecasting model based on long short-term memory. J Hydrol 589:125359
Citakoglu H, Babayigit B, Haktanir NA (2020) Solar radiation prediction using multi-gene genetic programming approach. Theor Appl Climatol 142(3):885–897
Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398(3-4):292–302
Cobaner M, Citakoglu H, Kisi O, Haktanir T (2014) Estimation of mean monthly air temperatures in Turkey. Comput Electron Agric 109:71–79
Cobaner M, Babayigit B, Dogan A (2016) Estimation of groundwater levels with surface observations via genetic programming. J-Am Water Works Assoc 108(6):E335–E348
Dong D, Sheng Z, Yang T (2018) Wind power prediction based on recurrent neural network with long short-term memory units. In: 2018 International Conference on Renewable Energy and Power Engineering (REPE). IEEE, pp 34–38.
Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput & Applic 21(1):171–187
Gandomi AH, Roke DA (2015) Assessment of artificial neural network and genetic programming as predictive tools. Adv Eng Softw 88:63–72
Geng D, Zhang H, Wu H (2020) Short-term wind speed prediction based on principal component analysis and LSTM. Appl Sci 10(13):4416
Guan L, Yang J, Bell JM (2007) Cross-correlation between weather variables in Australia. Build Environ 42:1054–1070
Király A, Jánosi IM (2002) Stochastic modeling of daily temperature fluctuations. Phys Rev E 65(5):051102
Kisi O, Sanikhani H (2015) Prediction of long-term monthly precipitation using several soft computing methods without climatic data. Int J Climatol 35(14):4139–4150
Kisi O, Shiri J (2014) Prediction of long-term monthly air temperature using geographical inputs. Int J Climatol 34(1):179–186
Kisi O, Sanikhani H, Zounemat-Kermani M, Niazi F (2015) Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Comput Electron Agric 115:66–77
Kisi O, Demir V, Kim S (2017) Estimation of long-term monthly temperatures by three different adaptive neuro-fuzzy approaches using geographical inputs. J Irrig Drain Eng 143(12):04017052
Kocijan J, Ažman K, & Grancharova A (2007). The concept for Gaussian process model based system identification toolbox. In Proceedings of the 2007 international conference on Computer systems and technologies (pp. 1-6).
Koza JR (1992). Genetic programming: on the programming of computers by means of natural selection (Vol. 1). MIT press.
Kumar B, Jha A, Deshpande V, Sreenvinasulu G (2014) Regression model for sediment transport problems using multi-gene symbolic genetic programming. Comput Electron Agric 103:82–90
Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017) Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut 231:997–1004
Li Q, Hao H, Zhao Y, Geng Q, Liu G, Zhang Y, Yu F (2020) GANs-LSTM model for soil temperature estimation from meteorological: a new approach. IEEE Access 8:59427–59443
Madár J, Abonyi J, Szeifert F (2005) Genetic programming for the identification of nonlinear input− output models. Ind Eng Chem Res 44(9):3178–3186
Malik A, Kumar A, Kim S, Kashani MH, Karimi V, Sharafati A, Ghorbani MA, al-Ansari N, Salih SQ, Yaseen ZM, Chau KW (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: Application of multiple learning artificial intelligence model. Eng Applic Comp Fluid Mech 14(1):323–338
Malik A, Tikhamarine Y, Souag-Gamane D, Rai P, Sammen SS, & Kisi O (2021). Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction. Meteorol Atm Physics 1-19.
Mehdizadeh S (2018) Assessing the potential of data-driven models for estimation of long-term monthly temperatures. Comput Electron Agric 144:114–125
Mehdizadeh S, Behmanesh J, Khalili K (2017) Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data. Environ Earth Sci 76(8):325
Mehr AD, Gandomi AH (2021) MSGP-LASSO: an improved multi-stage genetic programming model for streamflow prediction. Inf Sci 561:181–195
Mirasgedis S, Sarafidis Y, Georgopoulou E, Lalas DP, Moschovits M, Karagiannis F, Papakonstantinou D (2006) Models for mid-term electricity demand forecasting incorporating weather influences. Energy 31(2-3):208–227
Muduli K, Das SK (2013) CPT-based seismic liquefaction potential evaluation using multi-gene genetic programming approach. Indian Geotecnical J 1:86–93
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290
Nazari A, Rajeev P, Sanjayan JG (2015) Modelling of upheaval buckling of offshore pipeline buried in clay soil using genetic programming. Eng Struct 101:306–317
Neal RM, Bayesian Learning for Neural Networks (1996) Springer, 118th edn. Lecture Notes in Statistics, New York
Ododo JC (1997) Prediction of solar radiation using only maximum temperature and relative humidity: South-east and north-east Nigeria. Energy Convers Manag 38(18):1807e14
Ododo JC, Sulaiman AT, Aidan J, Yuguda MM, Ogbu FA (1995) The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria. Renew Energy 6(7):751e63
Paniagua-Tineo A, Salcedo-Sanz S, Casanova-Mateo C, Ortiz-García EG, Cony MA, Hernández-Martín E (2011) Prediction of daily maximum temperature using a support vector regression algorithm. Renew Energy 36(11):3054–3060
Pardo A, Meneu V, Valor E (2002) Temperature and seasonality influences on Spanish electricity load. Energy Econ 24(1):55–70
Poff NL, Tokar S, Johnson P (1996) Stream hydrological and ecological responses to climate change assessed with an artificial neural network. Limnol Oceanogr 41(5):857–863
Proceedings of the International Conference on Industrial Engineering and Operations Management. Riyadh, Saudi Arabia, pp 406–412.
Qi Y, Zhou Z, Yang L, Quan Y, Miao Q (2019) A decomposition-ensemble learning model based on LSTM neural network for daily reservoir inflow forecasting. Water Resour Manag 33(12):4123–4139
Qin Y, Li K, Liang Z, Lee B, Zhang F, Gu Y, Zhang L, Wu F, Rodriguez D (2019) Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal. Appl Energy 236:262–272
Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468
Qu X, Xiaoning K, Chao Z, et al (2016) Short-term prediction of wind power based on deep long short-term memory. In: 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, pp 1148–1152.
Rahimikhoob A (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew Energy 35(9):2131–2135
Rasmussen CE (2003) Gaussian processes in machine learning. In: In Summer school on machine learning. Springer, Berlin, pp 63–71
Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2):571–576
Sariturk B, Bayram B, Duran Z, Seker DZ (2020) Feature extraction from satellite images using Segnet and fully convolutional networks (FCN). Int J Eng Geosci 5(3):138–143
Sattari MT, Apaydin H, Band SS, Mosavi A, Prasad R (2021) Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrol Earth Syst Sci 25(2):603–618
Searson DP; Leahy DE; & Willis MJ, (2010). 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.
Su H, Li X, Yang B, Wen Z (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res-Atmos 106(D7):7183–7192
Tikhamarine Y, Souag-Gamane D, Kisi O (2019) 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):1–20
Yakut E, Suzulmus S (2020) 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):1–36
Yin J, Deng Z, Ines AV, Wu J, Rasu E (2020) 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 242:106386
Zahroh S, Hidayat Y, Pontoh RS (2019) Modeling and forecasting daily temperature in Bandung. In.
Zaytar MA, El Amrani C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int J Comput Appl 143(11):7–11
Zhai W, Cheng C (2020) A long short-term memory approach to predicting air quality based on social media data. Atmos Environ 237:117411
Zhang Q, Wang H, Dong J, Zhong G, Sun X (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett 14(10):1745–1749
Zhang J, Cao X, Xie J, Kou P (2019) An improved long short-term memory model for dam displacement prediction. Math Probl Eng 2019:6792189
Zhang CJ, Wang HY, Zeng J, Ma LM, Guan L (2020) Tiny-RainNet: a deep convolutional neural network with bi-directional long short-term memory model for short-term rainfall prediction. Meteorol Appl 27(5):e1956
Zhu S, Luo X, Yuan X, Xu Z (2020) An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stoch Env Res Risk A 34(9):1313–1329
Author information
Authors and Affiliations
Civil Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey
Hatice Citakoglu
- Hatice Citakoglu
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toHatice Citakoglu.
Ethics declarations
Ethics approval
The author paid attention to the ethical rules in the study. There is no violation of ethics.
Consent for publication
If this study is accepted, it can be published in the Arabian Journal of Geosciences.
Conflict of interest
The author declares no conflicts of interest.
Additional information
Responsible Editor: Zhihua Zhang
Rights and permissions
About this article
Cite this article
Citakoglu, H. Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey.Arab J Geosci14, 2131 (2021). https://doi.org/10.1007/s12517-021-08484-3
Received:
Accepted:
Published:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative