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Application of feed forward and cascade forward neural network models for prediction of hourly ambient air temperature based on MERRA-2 reanalysis data in a coastal area of Turkey

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Abstract

Air temperature forecasting has been a vital climatic factor required for different applications in many areas such as energy, industry, agriculture, health, environment, and meteorology. This study compares the performances of two static neural networks (NNs) used for the prediction of hourly ambient air temperatures in a coastal area of Turkey. Thirteen parameters from Land Surface Diagnostics and Surface Flux Diagnostics Collections from the MERRA-2 reanalysis dataset including pressure, surface specific humidity, wind speed, wind direction, air density at surface, evaporation, planetary boundary layer height, total precipitable water vapor, total precipitation, total cloud area fraction, total column ozone, greenness fraction, and leaf area index were used as input parameters for the models. Feed-Forward Neural Network (FFNN) and Cascade Forward Neural Network (CFNN) models were applied to forecast hourly ambient air temperatures at 2 m height from the surface. The results indicated that the most accurate and reliable predictions were obtained by the CFNN model with 30 neurons, while the lowest prediction performance was obtained by the FFNN model with 5 neurons. The root mean squares error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) values for training (and testing) of the CFNN model with 30 neurons were 0.358 (0.376), 0.273 (0.283), and 0.997 (0.992), respectively, whereas the same parameters were 0.430 (0.447), 0.334 (0.343), and 0.996 (0.989) for the FFNN model with 5 neurons. The CFNN model had a lower RMSE and MAE, and a higher R2 than the FFNN model. These results showed that increasing the number of neurons of hidden layers from 5 to 30 provided better model performance.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abujazar MS, Fatihah S, Ibrahim IA, Kabeel AE, Sharil S (2018) Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model. J Clean Prod 170(2018):147–159

    Article  Google Scholar 

  • Acı M, Avcı M (2016) Artificial neural network approach for atomic coordinate prediction of carbon nanotubes. Appl Phys A 122(631):1–14

    Google Scholar 

  • AERONET (AErosol RObotic NETwork) (2020) https://aeronet.gsfc.nasa.gov/cgi-bin/type_one_station_ opera_v2_new?site=IMS-METU-ERDEMLI&nachal=2&level=1. Accessed: May 2020

  • Alkhasawneh MS, Tay LT (2018) A hybrid intelligent system integrating the cascade forward neural network with elman neural network. Arab J Sci Eng 43:6737–6749

    Article  Google Scholar 

  • Attoue N, Shahrour I, Younes R (2018) Smart building: use of the artificial neural network approach for indoor temperature forecasting. Energies 11(395):1–12

    Google Scholar 

  • Badde DS, Gupta AK, Patki VK (2012) Cascade and feed forward back propagation artificial neural network models for prediction of compressive strength of ready mix concrete. IOSR J Mech Civil Eng 3(26):01–06

    Google Scholar 

  • Beik J, Asadi M, Mirrahimi M et al (2019) An image-based computational modeling approach for prediction of temperature distribution during photothermal therapy. Appl Phys B 125(213):1–14

    Google Scholar 

  • Bell J, Case J, Lafontaine F et al (2012) Evaluating the impacts of NASA/SPoRT daily greenness vegetation fraction on land surface model and numerical weather forecasts. Preprints. In: 16th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, New Orleans, LA, Amer. Meteor. Soc. p 247

  • Ben-Nakhi AE, Mahmoud MA (2004) Cooling load prediction for buildings using general regression neural networks. Energy Convers Manag 45:2127–2141

    Article  Google Scholar 

  • Berens P (2009) CircStat: a MATLAB toolbox for circular statistics. J Stat Softw 31(10):1–21

    Article  Google Scholar 

  • Bertolaccini L, Solli P, Pardolesi A, Pasini A (2017) An overview of the use of artificial neural networks in lung cancer research. J Thorac Dis 9(4):924–931

    Article  Google Scholar 

  • Bouketta S, Bouchahm Y (2020) Numerical evaluation of urban geometry’s control of wind movements in outdoor spaces during winter period Case of Mediterranean Climate. Renewable Energy 146(2020):1062–1069

    Article  Google Scholar 

  • Bre F, Gimenez JM, Fachinotti VD (2018) Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energy Buildings 158:1429–1441

    Article  Google Scholar 

  • Camia A, Bovio G, Aguado I, Stach N (1999) Meteorological fire danger indices and remote sensing. In Remote sensing of large wildfires. Springer, Berlin/Heidelberg, Germany, pp 39–59

  • Chattopadhyay S, Jhajharia D, Chattopadhyay G (2011) Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule-Walker equation based approach. Meteorol Appl 18:70–82

    Article  Google Scholar 

  • Chen S-M, Hwang J-R (2000) Temperature prediction using fuzzy time series. IEEE Trans Syst Man Cybern Part B 30(2):263–275

    Article  Google Scholar 

  • Dang R, Li H, Liu Z et al (2016) Statistical analysis of relationship between daytime Lidar-Derived planetary boundary layer height and relevant atmospheric variables in the semiarid region in Northwest China. Adv Meteorology 2016(5375918):1–13

    Article  Google Scholar 

  • Dombaycı ÖA, Gölcü M (2009) Daily means ambient temperature prediction using artificial neural network method: a case study of Turkey. Renewable Energy 34(4):1158–1161

    Article  Google Scholar 

  • Downs TD, Mardia KV (2002) Circular regression. Biometrika 89:689–697

    Article  Google Scholar 

  • Elbita A, Qahwaji R, Ipson S, Sharif MS, Ghanchi F (2014) Preparation of 2D sequences of corneal images for 3D model building. Comput Methods Programs Biomed 114(2):194–205

    Article  Google Scholar 

  • Erdemir D, Ayata T (2017) Prediction of temperature decreasing on a green roof by using artificial neural network. Appl Therm Eng 112:1317–1325

    Article  Google Scholar 

  • Ganesh SS, Arulmozhivarman P, Tatavarti VSNR (2018) Prediction of PM2.5 using an ensemble of artificial neural networks and regression models. J Amb Intel Hum Comp. https://doi.org/10.1007/s12652-018-0801-8

    Article  Google Scholar 

  • Garcia MA, Balenzategui J (2004) Estimation of photovoltaic module yearly temperature and performance based on nominal operation cell temperature calculations. Renew Energy 29:1997–2010

    Article  Google Scholar 

  • Hardalaç F, Basaranoglu M, Yüksel M, Kutbay U et al (2015) The rate of mucosal healing by azathioprine therapy and prediction by artificial systems. Turk J Gastroenterol 26:315–321

    Article  Google Scholar 

  • Hedayat A, Davilu H, Barfrosh AA, Sepanloo K (2009) Estimation of research reactor core parameters using cascade feed forward artificial neural networks. Prog Nucl Energy 51(6–7):709–718

    Article  Google Scholar 

  • Jammalamdaka SR, SenGupta A (2001) Topics in circular statistics. World Scientific, New York

    Book  Google Scholar 

  • Karaca Y (2016) Case study on artificial neural networks and applications. Appl Math Sci 10(45):2225–2237

    Google Scholar 

  • Kazem HA, Chaichan MT (2015) Effect of humidity on photovoltaic performance based on experimental study. Int J Appl Eng Res 10(23):43572–43577

    Google Scholar 

  • Kloog I, Nordio F, Coull BA, Schwartz J (2014) Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens Environ 150(2014):132–139

    Article  Google Scholar 

  • Kumar GS, Rajasekhar K (2017) Performance analysis of Levenberg-Marquardt and Steepest Descent algorithms based ANN to predict compressive strength of SIFCON using manufactured sand. Eng Sci Technol Int J 20(2017):1396–1405

    Google Scholar 

  • Lazzús JA, Vega-Jorquera P, Salfate I et al (2020) Variability and forecasting of air temperature in Elqui Valley (Chile). Earth Sci Inform 13:1411–1425

    Article  Google Scholar 

  • Lee S, Lee YS, Son Y (2020) Forecasting daily temperatures with different time interval data using deep neural networks. Appl Sci 10:1609

    Article  Google Scholar 

  • Lu E, Tu J (2021) Relative importance of surface air temperature and density to interannual variations in monthly surface atmospheric pressure. Int J Climatol 41(S1):E819–E831

    Article  Google Scholar 

  • Mardia KV, Jupp PE (2000) Directional statistics. John Wiley & Sons

    Google Scholar 

  • Matuszko D, Węglarczyk S (2013) Effect of cloudiness on long-term variability in air temperature in Krakow. Int J Climatol 34:145–154

    Article  Google Scholar 

  • Mba L, Meukam P, Kemajou A (2016) Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Buildings 121:32–42

    Article  Google Scholar 

  • Memon RA, Leung DYC, Liu CH (2010) Effects of building aspect ratio and wind speed on air temperatures in urban-like street canyons. Build Environ 45(1):176–188

    Article  Google Scholar 

  • Mirgol B, Nazari M (2018) Possible scenarios of winter wheat yield reduction of Dryland Qazvin Province, Iran, based on prediction of temperature and precipitation till the end of the century. Climate 6(78):1–14

    Google Scholar 

  • Narad S, Chavan P (2016) Cascade forward back-propagation neural network based group authentication using (n, n) secret sharing scheme. Procedia Comp Sci 78:185–191

    Article  Google Scholar 

  • NASA (National Aeronautics and Space Administration) (2020) Goddard Space Flight Center. https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/, Accessed: May 2020

  • Nassif WG, Jasim FH, Al-Taai OT (2021) Analysis of air temperature, relative humidity and evaporation over Iraq using ECMWF reanalysis. Indian J Ecology 48(2):446–452

    Google Scholar 

  • Ozdemir E, Tuna Tuygun G, Elbir T (2020) Application of aerosol classification methods based on AERONET version 3 product over eastern Mediterranean and Black Sea. Atmos Pollut Res 11:2226–2243

    Article  Google Scholar 

  • Potocnik P, Vidrih B, Kitanovski A et al (2019) Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings. Build Simul 12:1077–1093

    Article  Google Scholar 

  • Pwasong A, Sathasivam S (2016) A new hybrid quadratic regression and cascade forward backpropagation neural network. Neurocomputing 182:197–209

    Article  Google Scholar 

  • Raible CC, Bischof G, Fraedrich K et al (1999) Statistical single-station short-term forecasting of temperature and probability of precipitation: area interpolation and NWP combination. Weather Forecast 14(2):203–214

    Article  Google Scholar 

  • Rajendra P, Murthy KVN, Subbarao A et al (2019) Use of ANN models in the prediction of meteorological data. Model Earth Syst Environ 5:1051–1058

    Article  Google Scholar 

  • Rajini NH, Bhavani R (2013) Computer aided detection of ischemic stroke using segmentation and texture features. Measurement 46(6):1865–1874

    Article  Google Scholar 

  • Roy DS (2020) Forecasting the air temperature at a weather station using deep neural networks. Procedia Comp Sci 178:38–46

    Article  Google Scholar 

  • Salcedo-Sanz S, Deo RC, Carro-Calvo L et al (2016) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor Appl Climatol 125:13–25

    Article  Google Scholar 

  • Schneider A, Schuh A, Maetzel FK et al (2008) Weather-induced ischemia and arrhythmia in patients undergoing cardiac rehabilitation: another difference between men and women. Int J Biometeorol 52:535–547

    Article  Google Scholar 

  • Shin KH, Jung JW, Chang KH et al (2021) Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory. 1:22

  • Takeuchi Y, Endo Y, Murakami S (2008) High correlation between winter precipitation and air temperature in heavy-snowfall areas in Japan. Ann Glaciol 49:7–10

    Article  Google Scholar 

  • Tengeleng S, Armand N (2014) Performance of using cascade forward back propagation neural networks for estimating rain parameters with rain drop size distribution. Atmosphere 5(2):454–472

    Article  Google Scholar 

  • Tran TTK, Bateni SM, Ki SJ, Vosoughifar HA (2021) Review of neural networks for air temperature forecasting. Water 13(9):1294. https://doi.org/10.3390/w13091294

    Article  Google Scholar 

  • Tuna Tuygun G, Elbir T (2020) Long-term temporal analysis of the columnar and surface aerosol relationship with planetary boundary layer height at a southern coastal site of Turkey. Atmos Pollut Res 11:2259–2269

    Article  Google Scholar 

  • Ustaoglu B, Cigizoglu HK, Karaca M (2008) Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Met Apps 15:431–445

    Article  Google Scholar 

  • Vaidya M, Matsagar S, Mandavkar M et al (2019) Intelligent traffic monitoring system using IOT. Int J Sci Res Eng Trends 5(3):756–760

    Google Scholar 

  • Wang S, Kim S, Peiris TB (2018) An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study. J Appl Stat 45(3):474–486

    Article  Google Scholar 

  • Warsito B, Santoso R, Suparti YH (2018) Cascade forward neural network for time series prediction. J Phys: Conf Ser 1025(2018):1–8

    Google Scholar 

  • Xing L, Li L, Gong J, Ren C et al (2018) Daily soil temperatures predictions for various climates in United States using data-driven model. Energy 160:430–440

    Article  Google Scholar 

  • Yang J, Ma J (2019) Feed-forward neural network training using sparse representation. Expert Syst Appl 116:255–264

    Article  Google Scholar 

  • Yuan X, Chen C, Yuan Y et al (2015) Short-term wind power prediction based on LSSVM–GSA model. Energy Convers Manage 101:393–401

    Article  Google Scholar 

  • Zhao Z, Qin J, He Z et al (2020) Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-08948-1

    Article  Google Scholar 

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Acknowledgements

We are thankful to all of our students (Gizem Tuna Tuygun, Selin Karslioglu, and Elif Tezcan) at the Dokuz Eylul University for downloading data from MERRA-2.

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Authors and Affiliations

  1. Department of Computer Technologies, Bergama Vocational School, Dokuz Eylul University, Bergama-Izmir, Turkey

    Serdar Gündoğdu

  2. Department of Environmental Engineering, Faculty of Engineering, Dokuz Eylul University, Buca-Izmir, Turkey

    Tolga Elbir

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  1. Serdar Gündoğdu
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  2. Tolga Elbir
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Correspondence to Serdar Gündoğdu.

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Gündoğdu, S., Elbir, T. Application of feed forward and cascade forward neural network models for prediction of hourly ambient air temperature based on MERRA-2 reanalysis data in a coastal area of Turkey. Meteorol Atmos Phys 133, 1481–1493 (2021). https://doi.org/10.1007/s00703-021-00821-1

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