Analisis dan peramalan siri masa suhu menggunakan pendekatan kalut

Nor Zila Abd Hamid

QR Code Link :
Type :	article
Subject :	G Geography (General)
Main Author :	Nor Zila Abd Hamid
Additional Authors :	Munirah Bahari
Title :	Analisis dan peramalan siri masa suhu menggunakan pendekatan kalut

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

Analisis dan peramalan siri masa suhu adalah penting kerana perubahan suhu boleh membawa kesan serius kepada kesihatan. Kajian ini dijalankan bertujuan menganalisis dan meramal siri masa suhu di Jerantut, Pahang, Malaysia dengan menggunakan pendekatan kalut. Pemodelan kalut dibahagikan kepada dua tahap; pembinaan semula ruang fasa dan proses peramalan. Melalui pembinaan semula ruang fasa, data skalar satu matra dibina semula menjadi ruang fasa multimatra. Ruang fasa multimatra ini digunakan untuk mengesan kehadiran dinamik kalut melalui kaedah plot ruang fasa dan kaedah Cao. Keputusan menunjukkan bahawa siri masa yang diperhatikan bersifat kalut. Oleh itu, peramalan satu jam ke hadapan dibina melalui kaedah penghampiran purata setempat yang merupakan kaedah peramalan asas menggunakan pendekatan kalut. Nilai pekali korelasi yang diperoleh adalah 0.9443. Nilai yang menghampiri satu ini menunjukkan hasil peramalan yang bagus dengan merupakan refleksi bahawa siri masa yang diramal dan siri masa yang sebenar adalah hampir antara satu sama lain. Oleh itu, pendekatan kalut merupakan satu kaedah alternatif yang bagus untuk digunakan bagi meramal siri masa suhu. Keputusan ini diharapkan boleh membantu merealisasikan perancangan strategik Jabatan Meteorologi Malaysia dan Jabatan Alam Sekitar seperti meningkatkan keberkesanan perkhidmatan cuaca bagi mengurangkan risiko bencana dan memperkukuhkan perkhidmatan iklim bagi kemakmuran negara.

References

1. Adenan N. H., Hamid N. Z. A., Mohamed Z. & Nooraini M. S. M. 2017. A pilot study of river flow prediction in urban area based on phase space reconstruction. Dlm. AIP Conference Proceedings 1870, 040011. https://doi.org/10.1063/1.4995843

2. Adenan N. H. & Noorani M. S. M. 2015. Peramalan data siri masa aliran sungai di dataran banjir dengan menggunakan pendekatan kalut. Sains Malaysiana 44(3): 463–471.

3. Adiwijaya, Wisesty U. N. & Nhita F. 2014. Study of line search techniques on the modified backpropagation for forecasting of weather data in Indonesia. Far East Journal of Mathematical Sciences 86(2): 139–148.

4. Awang N. R., Ramli A., Mohammed N. I. & Yahaya A. S. 2013. Time series evaluation of ozone concentrations in Malaysia based on location of monitoring stations. International Journal of Engineering and Technology 3(3): 390–394.

5. Cao L. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena 110(1–2): 43–50. https://doi.org/10.1016/S0167-2789(97)00118-8

6. Cheng J., Xie M. Y., Zhao K. F., Wu J. J., Xu Z. W., Song J., Zhao D. S., Li K. S., Wang X., Yang H. H., Wen L. Y., Su H. & Tong S. L. 2017. Impacts of ambient temperature on the burden of bacillary dysentery in urban and rural Hefei, China. Epidemiology and Infection 145(8): 1567–1576. https://doi.org/10.1017/S0950268817000280

7. Domenico M. De, Ghorbani M. A., Makarynskyy O., Makarynska D. & Asadi, H. 2013. Chaos and reproduction in sea level. Applied Mathematical Modelling 37(6): 3687–3697. https://doi.org/10.1016/j.apm.2012.08.018

8. Echi I. M., Tikyaa E. V. & Isikwue B. C. 2015. Dynamics of daily rainfall and temperature in Makurdi. International Journal of Science and Research 4(7): 493–499.

9. Fu Q., Liu Y., Li T., Liu D. & Cui S. 2017. Analysis of irrigation water use efficiency based on the chaos features of a rainfall time series. Water Resources Management 31(6): 1961–1973. https://doi.org/10.1007/s11269-017-1624-7

10. Hamid N. Z. A., Adenan N. H. & Nooraini M. S. M. 2017. Forecasting and analyzing high o 3 time series in educational area through an improved chaotic approach. Dlm. AIP Conference Proceedings 1870, 040035. https://doi.org/10.1063/1.4995867

11. Hamid N. Z. A. & Noorani M. S. M. 2014. A pilot study using chaotic approach to determine characteristics and forecasting of pm10 concentration time series. Sains Malaysiana 43(3): 475–481.

12. Ibrahim M. H., Zulkifli M. R., Ihsan M., Ismail M., Kalsum N., Isa M. & Adnan M. 2016. Impact of urbanization on temperature distribution in Malaysia: A case study of Rawang, Selangor. Malaysia Journal of Society and Space 12(5): 83–93.

13. Indira P., Inbanathan S. S. R., Selvaraj R. S. & Suresh A. A. 2016. Forecasting daily maximum temperature of chennai using nonlinear prediction approach. Indian Journal of Science and Technology 9(39): 1-6 https://doi.org/10.17485/ijst/2016/v9i39/100776

14. Islam M. N. & Sivakumar B. 2002. Characterization and prediction of runoff dynamics: A nonlinear dynamical view. Advances in Water Resources 25(2): 179–190. https://doi.org/10.1016/S0309-1708(01)00053-7

15. Li T.-Y., & Yorke J. A. 1975. Period three implies chaos. The American Mathematical Monthly 82(10): 985–992.

16. Lorenz E. N. 1963. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences 20(2): 130–141. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

17. Pau S., Wolkovich E. M., Cook B. I., Nytch C. J., Regetz J., Zimmerman J. K. & Wright S. J. 2013. Clouds and temperature drive dynamic changes in tropical flower production. Nature Climate Change 3(7): 838–842. https://doi.org/10.1038/nclimate1934

18. Regonda S., Rajagopalan B., Lall U., Clark M. & Moon Y.-I. 2005. Local polynomial method for ensemble forecast of time series. Nonlinear Processes in Geophysics 12: 397–406. https://doi.org/10.5194/npg-12-397-2005

19. Scott C. E., Monks S. A., Spracklen D. V., Arnold S. R., Forster P. M., Rap A., Aijala M., Artaxo P., Carslaw K. S., Chipperfield M. P., Ehn M., Gilardoni S., Heikkinen L., Kulmala M., Petaja T., Reddington C. L. S., Rizzo L. V., Swietlicki E., Vignati E. & Wilson C. 2018. Impact on short-lived climate forcers increases projected warming due to deforestation. Nature Communications 9(1): 1–9. https://doi.org/10.1038/s41467-017-02412-4

20. Takens F. 1981. Detecting strange attractors in turbulence. Dlm. Rand D. & Young L.S. (pnyt). Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics 898, pp. 366–381. Berlin: Springer. https://doi.org/10.1007/bfb0091924

21. Thinh N. C., Shimono H., Kumagai E. & Kawasaki M. 2017. Effects of elevated CO 2 concentration on growth and photosynthesis of Chinese yam under different temperature regimes. Plant Production Science 1008(March): 1–10. https://doi.org/10.1080/1343943X.2017.1283963

22. Tol R. S. J. 2018. The economic impacts of climate change. Review of Environmental Economics and Policy 12(1): 4–25. https://doi.org/10.1093/reep/rex027

23. Wichmann J. 2017. Heat effects of ambient apparent temperature on all-cause mortality in Cape Town, Durban and

24. Johannesburg, South Africa: 2006-2010. Science of the Total Environment, 587–588, 266–272. https://doi.org/10.1016/j.scitotenv.2017.02.135

25. Wilson A., Reich B. J., Nolte C. G., Spero T. L., Hubbell B. & Rappold A. G. 2016. Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone–temperature risk surfaces. Journal of Exposure Science and Environmental Epidemiology 27(1): 1–7. https://doi.org/10.1038/jes.2016.14 www.bharian.com.my. https://www.bharian.com.my/rencana/muka10/2019/04/547871/suhu-melampau-jejaskesihatan [7 Disember 2018].

26. Zeng J., Lu C. & Deng Q. 2017. Prenatal exposure to diurnal temperature variation and early childhood pneumonia. Journal of Thermal Biology 65(February): 105–112. https://doi.org/10.1016/j.jtherbio. 2017.02.012

27. Zhan Z., Zhao Y., Pang S., Zhong X., Wu C. & Ding Z. 2017. Temperature change between neighboring days and mortality in United States: A nationwide study. Science of the Total Environment 584: 1152–1161. https://doi.org/10.1016/j.scitotenv.2017.01.177

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page