Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 18/2021

02.01.2021 | Review

Application of neural network in prediction of temperature: a review

verfasst von: Charles Johnstone, Emmanuel D. Sulungu

Erschienen in: Neural Computing and Applications | Ausgabe 18/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Nächster Artikel DeepVisDroid: android malware detection by hybridizing image-based features with deep learning techniques

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
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
2.
Abhishek K, Singh MP, Ghosh S, Anand A (2012) Weather forecasting model using artificial neural network. Proced Technol 4:311–318
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
4.
Andri A, Mahardhika P, Edwin L, Yew SO (2019) Devdan: deep evolving denoising autoencoder. Neurocomputing 390:297–314
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
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
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
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
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
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
13.
Culclasure A (2013) Using neural networks to provide local weather forecasts. Southern University, Georgia
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
16.
Dombaycı ÖA, Önder Ç (2006) Estimation of hourly mean ambient temperature with artificial neural network. Math Comput Appl 11(3):215–224
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
20.
Feng J, Lu S (2019) Performance analysis of various activation functions in artificial neural networks. J Phys: Conf Ser 1237:022030
21.
George RK (2001) Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Anal 47(3):1737–1748MathSciNetMATH
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
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
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
25.
Hayati M, Mohebi Z (2007) Application of artificial neural networks for temperature forecasting. Int J Electr, Electron Sci Eng 1(4):1–5
26.
Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson Education Inc, New Jersey
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
29.
Hussain N (2012) The Jordan Pi - Sigma neural network for temperature prediction. Universiti Tun Hussein Onn, Malaysia
30.
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
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
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
35.
36.
Kon M, Plaskota L (2000) Information complexity of neural networks. Neural Netw 13(3):365–375MATH
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
38.
Kumar P, Kashyap P, Javeed (2013) Temperature forecasting using artificial neutral networks ( ANN ). J Hill Agric 4(2):110–112
39.
Lynch P (2016) The emergence of numerical weather prediction. Cambridge University Press, CambridgeMATH
40.
Lynnae S (2015) What is weather forecasting? Britanic Educational publishing, New York
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
42.
Matuszko D, Stanisław W (2015) Relationship between sunshine duration and air temperature. Int J Climatol 3653:3640–3653
43.
Meda-campaña JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973
44.
Mekanik F (2015) Seasonal rainfall forecasting using large scale climate drivers: an artificial intelligence approach. Swinburne University of Technology, Melbourne
45.
Mohita AS (2012) Comparative study of forecasting models based on weather parameters. Shobhit university, Uttar Pradesh
46.
Moradi G, Mohadesi M, Moradi MR (2013) Prediction of wax disappearance temperature using artificial neural networks. J Petrol Sci Eng 108:74–81
47.
Nagendra SMSK (2006) Artificial neural network approach for modeling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecological Model 190:99–115
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
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):455–464MathSciNet
51.
Papantoniou S, Kolokotsa D (2015) Prediction of outdoor air temperature using neural networks: application in 4 European cities. Energy Build 114:72–79
52.
Paras SM (2012) A simple weather forecasting model using mathematical regression. Indian Res J Ext Educ Spec Issue 1:161–168
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
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
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
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
58.
McClelland JL, Rumelhart DE (1989) Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT press, Bradford
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 86:10–17MATH
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
61.
de Rubio J, J (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309
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):2311–2320MathSciNetMATH
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
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
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
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
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
70.
Smith BA (2006) Air temperature prediction using artificial neural networks. The University of Georgia, Georgia
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
72.
Tolstykh M, Frolov A (2005) Some current problems in numerical weather prediction. Izvestiya Atmos Ocean Phys 41:285–295MathSciNet
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
74.
Wooten RD (2011) Statistical analysis of the relationship between wind speed, pressure and temperature. J Appl Sci 11(15):2712–2722
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
Metadaten
Titel
Application of neural network in prediction of temperature: a review
verfasst von
Charles Johnstone
Emmanuel D. Sulungu
Publikationsdatum
02.01.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 18/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05582-3

Weitere Artikel der Ausgabe 18/2021

Neural Computing and Applications 18/2021 Zur Ausgabe

Original Article

Marine predators algorithm for parameters estimation of photovoltaic modules considering various weather conditions

Original Article

Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

Original Article

Sliding mode control-based fixed-time stabilization and synchronization of inertial neural networks with time-varying delays

Original Article

Walking pattern analysis using deep learning for energy harvesting smart shoes with IoT

Original Article

Enhancing trajectory prediction using sparse outputs: application to team sports

Original Article

Primate vision: a single layer perception

Premium Partner

    Bildnachweise