Skip to main content

Advertisement

Springer Nature Link
Log in

Application of neural network in prediction of temperature: a review

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The aim of this study was to review different literatures to assess the applicability of artificial neural network in predicting temperature. Temperature prediction as part of weather prediction involves the application of science and technology to predict the state of temperature for a future period in a specific location. Artificial neural network (ANN) has been found to be a promising tool to be used in temperature prediction because it is able to handle complex and nonlinear physical variables of the atmosphere. The use of ANN for prediction of weather elements has shown significant improvements in prediction and accuracy. The performance of the ANN model varies depending on the nature and number of input data used in training the network, the number of neurons in the hidden layer, architecture of a network, transfer function and on the training algorithms. The choice of ANN architecture and the type of data depend on the nature of the problem to be addressed. ANN is therefore found to be a powerful tool in predicting temperature of a specific place, provided input parameters of the model are well chosen.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Abbot J, Marohasy J (2014) Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178

    Google Scholar 

  2. Abhishek K, Singh MP, Ghosh S, Anand A (2012) Weather forecasting model using artificial neural network. Proced Technol 4:311–318

    Google Scholar 

  3. Ahmad R, Lazin NM, Samsuri SFM (2014) Neural network modeling and identification of naturally ventilated tropical greenhouse climates. Wseas Trans Syst Control 9:445–453

    Google Scholar 

  4. Andri A, Mahardhika P, Edwin L, Yew SO (2019) Devdan: deep evolving denoising autoencoder. Neurocomputing 390:297–314

    Google Scholar 

  5. Anekwe F, Onuchukwu C (2017) Study of the variation in weather parameters in some selected cities in Southern Nigeria. COOU J Multidiscip Stud 1:122–132

    Google Scholar 

  6. Ashrafi K, Shafiepour M, Ghasemi L, Najar AB (2012) Prediction of climate change induced temperature rise in regional scale using neural network. Int J Environ Res 6(3):677–688

    Google Scholar 

  7. Bani-ahmad S, Alshaer J, Al-oqily I (2014) Development of temperature-based weather forecasting models using neural networks and fuzzy logic. Int J Multimed Ubiquitous Eng 9(12):343–366

    Google Scholar 

  8. Cachim P (2010) Temperature prediction in timber using artificial neural networks. In Proceedings of 2010 world conference on timbre engineering pp 1–5

  9. Chaturvedi DK (2008) Factors Affecting the performance of artificial neural network models. Techniques and its Applications in Electrical Engineering, Soft Computing, pp 51–85

    Google Scholar 

  10. Chiang H, Chen M, Huang Y (2019) Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7:103255–103262

    Google Scholar 

  11. Chu WT, Ho KC, Borji A (2018) Visual weather temperature prediction. In Proceeding of 2018 IEEE Winter Conference on Applications of Computer Vision pp 234–241

  12. Coulibaly P, Dibike YB, Anctil F (2005) Downscaling precipitation and temperature with temporal neural networks. J Hydrometeorol 6(4):483–496

    Google Scholar 

  13. Culclasure A (2013) Using neural networks to provide local weather forecasts. Southern University, Georgia

    Google Scholar 

  14. Darj M, Dabh V, Prajapati H (2015) Rainfall forecasting using neural network: a survey. In Proceedings of International Conference on Advances in Computer Engineering and Applications (ICACEA) pp 706–713

  15. David N, Rajagopalan B, Zagona E (2003) Regression model for daily maximum stream temperature. J Environ Eng 129(7):667–674

    Google Scholar 

  16. Dombaycı ÖA, Önder Ç (2006) Estimation of hourly mean ambient temperature with artificial neural network. Math Comput Appl 11(3):215–224

    Google Scholar 

  17. Dorofki M, Elshafie AH, Jaafar O, Karim OA (2012) Comparison of artificial neural network transfer functions abilities to simulate extreme runoff data. In Proceeding of 2012 International Conference on Environment, Energy and Biotechnology 33 pp 39–44

  18. Doukim CA, Dargham JA, Chekima A (2010) Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique. In Proceedings of the 10th International Conference on Information Sciences, Signal Processing and Their Applications (ISSPA 10) pp 606–609

  19. Elias I, de Jose JR, Cruz DR, Ochoa G, Novoa JF, Martinez DI, Juarez CF (2020) Hessian with mini-batches for electrical demand prediction. Appl Sci 10(6):2036

    Google Scholar 

  20. Feng J, Lu S (2019) Performance analysis of various activation functions in artificial neural networks. J Phys: Conf Ser 1237:022030

    Google Scholar 

  21. George RK (2001) Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Anal 47(3):1737–1748

    MathSciNet  MATH  Google Scholar 

  22. Goswani K, Hazarika J, Patowary A (2017) Monthly temperature prediction based on arima model: a case study in Dibrugarh station of Assam, India. Int J Adv Res Comput Sci 8(8):292–298

    Google Scholar 

  23. Güldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir lake level forecasting. Water Resour Manage 24(1):105–128

    Google Scholar 

  24. Gustavo A, de Jose JR, Ricardo B, David RC, Garcia E, Juan FN, Alejandro Z (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8(1):46324–46334

    Google Scholar 

  25. Hayati M, Mohebi Z (2007) Application of artificial neural networks for temperature forecasting. Int J Electr, Electron Sci Eng 1(4):1–5

    Google Scholar 

  26. Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson Education Inc, New Jersey

    Google Scholar 

  27. Holmstrom M, Liu D, Vo C (2016) Machine learning applied to weather forecasting. Stanford University

  28. Hunter D, Yu H, Pukish M, Kolbusz J, Wilamowski B (2012) Selection of proper neural network sizes and architectures: a comparative study. IEEE Trans Industr Inf 8(2):228–240

    Google Scholar 

  29. Hussain N (2012) The Jordan Pi - Sigma neural network for temperature prediction. Universiti Tun Hussein Onn, Malaysia

    Google Scholar 

  30. Husain N, Ghazali R, Nawi M, Ismail L (2011) Pi-Sigma neural network for temperature forecasting in Batu Pahat. Commun Comput Inform Sci. https://doi.org/10.1007/978-3-642-22191-0_46

    Article  Google Scholar 

  31. IPCC (2007) Intergovernmental panel on climate change, Fourth Assesment Report: Climate Change 2007. Geneva

  32. Iseh AJ, Woma TY (2013) Weather forecasting models, methods and applications. Int J Eng Technol 2(12):1945–1957

    Google Scholar 

  33. Karume K, Banda E, Mubiru J, Majaliwa M (2007) Correlation between sunshine hours and climatic parameters at four location in Uganda. Tanz J Sci 33:93–100

    Google Scholar 

  34. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  35. Kiseľák J, Lu Y, Švihra J, Szépe P, Stehlík M (2020) “SPOCU”: scaled polynomial constant unit activation function. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05182-1

    Article  Google Scholar 

  36. Kon M, Plaskota L (2000) Information complexity of neural networks. Neural Netw 13(3):365–375

    MATH  Google Scholar 

  37. Krishna GV (2015) An integrated approach for weather forecasting based on data mining and forecasting analysis. Int J Comput Appl 120(11):26–29

    Google Scholar 

  38. Kumar P, Kashyap P, Javeed (2013) Temperature forecasting using artificial neutral networks ( ANN ). J Hill Agric 4(2):110–112

    Google Scholar 

  39. Lynch P (2016) The emergence of numerical weather prediction. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  40. Lynnae S (2015) What is weather forecasting? Britanic Educational publishing, New York

    Google Scholar 

  41. Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25:891–909

    Google Scholar 

  42. Matuszko D, Stanisław W (2015) Relationship between sunshine duration and air temperature. Int J Climatol 3653:3640–3653

    Google Scholar 

  43. Meda-campaña JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973

    Google Scholar 

  44. Mekanik F (2015) Seasonal rainfall forecasting using large scale climate drivers: an artificial intelligence approach. Swinburne University of Technology, Melbourne

    Google Scholar 

  45. Mohita AS (2012) Comparative study of forecasting models based on weather parameters. Shobhit university, Uttar Pradesh

    Google Scholar 

  46. Moradi G, Mohadesi M, Moradi MR (2013) Prediction of wax disappearance temperature using artificial neural networks. J Petrol Sci Eng 108:74–81

    Google Scholar 

  47. Nagendra SMSK (2006) Artificial neural network approach for modeling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecological Model 190:99–115

    Google Scholar 

  48. Nwankpa CE, Ijomah W, Gachagan A, Marshall S (2020) Activation functions: comparison of trends in practice and research for deep learning. In: 2nd international conference on computational sciences and technology, (INCCST)

  49. Ochanda OO (2016) Time series analysis and forecasting of monthly Air temperature changes in Nairobi Kenya. University of Nairobi, Nairobi

    Google Scholar 

  50. Panchal FS, Panchal M (2014) Review on methods of selecting number of hidden nodes in artificial neural network. Int J Comput Sci Mobile Comput 3(11):455–464

    MathSciNet  Google Scholar 

  51. Papantoniou S, Kolokotsa D (2015) Prediction of outdoor air temperature using neural networks: application in 4 European cities. Energy Build 114:72–79

    Google Scholar 

  52. Paras SM (2012) A simple weather forecasting model using mathematical regression. Indian Res J Ext Educ Spec Issue 1:161–168

    Google Scholar 

  53. Pedamonti D (2018) Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv (3).

  54. Radhika Y, Shashi M (2009) Atmospheric temperature prediction using support vector machines. Int J Comput Theory Eng 1:55–58

    Google Scholar 

  55. Ramesh K, Anitha R, Selvagopal P (2014) Linear regression based lead seven day maximum and minimum air temperature prediction in Chennai, India. Res J Appl Sci, Eng Technol 7(11):2306–2310

    Google Scholar 

  56. Reddy KR, Hodges HF, McKinion JM (1995) Carbon dioxide and temperature effects on pima cotton growth. Agr Ecosyst Environ 54(1–2):17–29

    Google Scholar 

  57. Ruano AE, Crispim EM, Conceição EZE, Lúcio MMJR (2005) Prediction of building’s temperature using neural networks models. Energy Build 38(6):682–694

    Google Scholar 

  58. McClelland JL, Rumelhart DE (1989) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT press, Bradford

    Google Scholar 

  59. Rajchakit G, Saravanakumar R, Choon KA, Hamid RK (2017) Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals. Neural Netw 86:10–17

    MATH  Google Scholar 

  60. Rajchakit G, Saravanakumar R (2018) Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays. Neural Comput Appl 29(2):483–492

    Google Scholar 

  61. de Rubio J, J (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Google Scholar 

  62. Saravanakumar R, Rajchakit G, Ali MS, Joo YH (2017) Extended dissipativity of generalised neural networks including time delays. Int J Syst Sci 48(11):2311–2320

    MathSciNet  MATH  Google Scholar 

  63. Saxena A, Verma N, Tripathi KC (2013) A review study of weather forecasting using artificial neural network approach. Int J Eng Res Technol 2(11):2029–2035

    Google Scholar 

  64. Shaker F, Monadjemi HA, Yazdanpanah H (2014) Comparing artificial neural networks and linear regression model in predicting soil surface temperature. Int J Sci Knowl 5(6):1–6

    Google Scholar 

  65. Shamisi MH, Al Assi AH, Hejase HAN (2009) Using MATLAB to develop artificial neural network models for predicting global solar radiation in Al Ain City – UAE. Engineering Education and Research Using MATLAB, 220–238

  66. Sharma A, Agarwal S (2012) Temperature prediction using wavelet neural natwork. Res J Inform Technol 4(1):22–30

    Google Scholar 

  67. Shibata K, Ikeda Y (2009) Effect of number of hidden neurons on learning in large-scale layered neural networks. In Proceedings of the ICROS-SICE International Joint Conference 2009 (ICCAS-SICE 2009) pp 5008–5013

  68. Shrestha RR, Theobald S, Nestmann F (2005) Simulation of flood flow in a river system using artificial neural networks. Hydrol Earth Syst Sci 9(4):313–321

    Google Scholar 

  69. Shrivastava G, Karmakar S, Manoj KK (2012) Application of artificial neural networks in weather forecasting: a comprehensive literature review. Int J Comput Appl 51(18):17–29

    Google Scholar 

  70. Smith BA (2006) Air temperature prediction using artificial neural networks. The University of Georgia, Georgia

    Google Scholar 

  71. Sundar C, Chitradevi M, Geetharamani G (2012) Classification of cardiotocogram data using neural network based machine learning technique. Int J Comput Appl 47(13):19–25

    Google Scholar 

  72. Tolstykh M, Frolov A (2005) Some current problems in numerical weather prediction. Izvestiya Atmos Ocean Phys 41:285–295

    MathSciNet  Google Scholar 

  73. Tyagi H, Shweta S, Pattanaik V (2016) Weather - temperature pattern prediction and anomaly identification using artificial neural network. Int J Comput Appl 140(3):15–21

    Google Scholar 

  74. Wooten RD (2011) Statistical analysis of the relationship between wind speed, pressure and temperature. J Appl Sci 11(15):2712–2722

    Google Scholar 

  75. Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using artificial neural networks in the mountainous Euphrates Basin. J Hydrol 410:134–140

    Google Scholar 

Download references

Acknowledgement

Authors wish to thank all members of department of Physics, University of Dodoma, for their constructive comments and support towards successful writing of this article.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Physics, College of Natural and Mathematical Sciences, The University of Dodoma, Dodoma, Tanzania

    Charles Johnstone

  2. Department of Natural Sciences, College of Science and Technical Education, Mbeya University of Science and Technology, Mbeya, Tanzania

    Emmanuel D. Sulungu

Authors
  1. Charles Johnstone
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Emmanuel D. Sulungu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The idea for the article was from Emmanuel D. Sulungu, the literature search was performed by CJ, and EDS drafted and critically revised the work.

Corresponding author

Correspondence to Emmanuel D. Sulungu.

Ethics declarations

Conflict of interest

Charles Johnstone and Emmanuel D. Sulungu declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnstone, C., Sulungu, E.D. Application of neural network in prediction of temperature: a review. Neural Comput & Applic 33, 11487–11498 (2021). https://doi.org/10.1007/s00521-020-05582-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05582-3

Keywords