Skip to main content

Meteorological Parameters Prediction Along Roads Between Two Cities for the Safest Itinerary Selection

  • Conference paper
  • First Online:
Innovations in Smart Cities Applications Edition 3 (SCA 2019)

Abstract

Adverse weather conditions are undesirable for drivers and can sometimes cause fatal accidents. In this article, we propose a driver assistance system, offering more secured routes as their weather conditions are more favorable compared to others.

Specifically, we propose a system able to forecast meteorological parameters based on the ARIMA models. These parameters play an important role on the classification of the degree of road safety of the three selected routes which connect Tangier and Tetouan cities. The meteorological data for the three routes are extracted from the MERRA-2 Web Service (Modern retrospective analysis for research and applications, version 2).

According to the prediction and classification results, the obtained ARIMA models are capable to assimilate the dynamics of meteorological data and produce important forecasts.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lamorski, K., Pastuszka, T., Krzyszczak, J., Sławiński, C., Witkowska-Walczak, B.: Soil water dynamic modeling using the physical and support vector machine methods. Vadose Zone J. 12(4) (2013). https://doi.org/10.2136/vzj2013.05.0085

  2. Baranowski, P., Krzyszczak, J., Sławiński, C., Hoffmann, H., Kozyra, J., Nieróbca, A., Siwek, K., Gluza, A.: Multifractal analysis of meteorological time series to assess climate impacts. Clim. Res. 65, 39–52 (2015)

    Article  Google Scholar 

  3. Murat, M., Malinowska, I., Hoffmann, H., Baranowski, P.: Statistical modeling of agrometeorological time series by exponential smoothing. Int. Agrophys. 30(1), 57–66 (2016)

    Article  Google Scholar 

  4. Hoffmann, H., Baranowski, P., Krzyszczak, J., Zubik, M., Sławiński, C., Gaiser, T., Ewert, F.: Temporal properties of spatially aggregated meteorological time series. Agric. For. Meteorol. 234, 247–257 (2017). https://doi.org/10.1016/j.agrformet.2016.12.012

    Article  Google Scholar 

  5. Fronzek, S., Pirttioja, N., Carter, T.R., Bindi, M., Hoffmann, H., Palosuo, T., Ruiz-Ramos, M., Tao, F., Trnka, M., Acutis, et al.: Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change. Agric. Syst. 159, 209–224 (2018). 10.1016/j.agsy.2017.08.004

    Google Scholar 

  6. Pirttioja, N., et al.: Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces. Clim. Res. 65, 87–105 (2015). https://doi.org/10.3354/cr01322

    Article  Google Scholar 

  7. Porter, J.R., Semenov, M.A.: Crop responses to climatic variation. Philos. Trans. R. Soc. B: Biol. Sci. 360(1463), 2021–2035 (2005)

    Article  Google Scholar 

  8. Ruiz-Ramos, M., et al.: Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a mediterranean environment. Agric. Syst. 159, 260–274 (2018). https://doi.org/10.1016/j.agsy.2017.01.009

    Article  Google Scholar 

  9. Krzyszczak, J., Baranowski, P., Zubik, M., Hoffmann, H.: Temporal scale influence on multifractal properties of agro-meteorological time series. Agric. For. Meteorol. 239, 223–235 (2017)

    Article  Google Scholar 

  10. Walczak, R.T., Witkowska-Walczak, B., Baranowski, P.: Soil structure parameters in models of crop growth and yield prediction. Phys. Submodels. Int. Agrophys. 11, 111–127 (1997)

    Google Scholar 

  11. Lobell, B.D., Sibley, A., Ortiz-Monasterio, J.I.: Extreme heat effects on wheat senescence in India. Nat. Clim. Change 2, 186–189 (2012)

    Article  Google Scholar 

  12. Semenov, M.A., Shewry, P.R.: Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci. Rep. 1, 66 (2011)

    Article  Google Scholar 

  13. Sillmann, J., Roeckner, E.: Indices for extreme events in projections of anthropogenic climate change. Clim. Change 86, 83–104 (2008)

    Article  Google Scholar 

  14. Lobell, D.B., Hammer, G.L., Mclean, G., Messina, C., Roberts, M.J., Schlenker, W.: The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013)

    Article  Google Scholar 

  15. El-Mallah, E.S., Elsharkawy, S.G.: Time-series modeling and short term prediction of annual temperature trend on Coast Libya using the box-Jenkins ARIMA Model. Adv. Res. 6(5), 1–11 (2016)

    Article  Google Scholar 

  16. Balyani, Y., Niya, G.F., Bayaat, A.: A study and prediction of annual temperature in Shiraz using ARIMA model. J. Geogr. Sp. 12(38), 127–144 (2014)

    Google Scholar 

  17. Anitha, K., Boiroju, N.K., Reddy, P.R.: Forecasting of monthly mean of maximum surface air temperature in India. Int. J. Statistika Mathematika 9(1), 14–19 (2014)

    Google Scholar 

  18. Muhammet, B.: The analyse of precipitation and temperature in Afyonkarahisar (Turkey) in respect of box-Jenkins technique. J. Acad. Soc. Sci. Stud. 5(8), 196–212 (2012)

    Google Scholar 

  19. Khedhiri, S.: Forecasting temperature record in PEI, Canada. Lett. Spat. Resour. Sci. 9, 43–55 (2014). https://doi.org/10.1007/s12076-014-0135-x

    Article  Google Scholar 

  20. Delignières, D.: Séries temporelles – Modèles ARIMA. Séminaire EA “Sport – Performance – Santé” Mars 2000

    Google Scholar 

  21. Box, G.E.P., Jenkins, G., Reinsel, G.: Time Series Analysis. Wiley Press, Hoboken (2008)

    Book  Google Scholar 

  22. Goude, Y.: Les processus AR et MA MAP-STA2 : Séries chronologiques 2018–2019

    Google Scholar 

  23. Devuyst, P.: Météorologie - Comprendre, interprêter, appliquer, Edité par A. DE VISSCHER EDITEUR/EDITIONS EYROLLES (1972)

    Google Scholar 

  24. Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013)

  25. Allach, S., Ahmed, M.B., Boudhir, A.A.: Detection of driver’s alertness level based on the Viola and Jones method and logistic regression analysis. Int. J. Intell. Enterp. 6(2–4), 356–368 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samir Allach .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Allach, S., Benamrou, B., Ahmed, M.B., Boudhir, A.A., Ouardouz, M. (2020). Meteorological Parameters Prediction Along Roads Between Two Cities for the Safest Itinerary Selection. In: Ben Ahmed, M., Boudhir, A., Santos, D., El Aroussi, M., Karas, İ. (eds) Innovations in Smart Cities Applications Edition 3. SCA 2019. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-37629-1_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37629-1_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37628-4

  • Online ISBN: 978-3-030-37629-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics