• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India
  • VIEWS 742
  • PDF 215

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i28.764

Year: 2022, Volume: 15, Issue: 28, Pages: 1372-1380

Original Article

Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India

Govind Verma1*, A Mehta2, S S Bhandari3

1Research scholar, Department of Computer Science, Kumaun University, India
2Associate Professor, Department of Computer Science, Convener and Head, Kumaun
University, India
3Assistant Professor, Department of Mechanical Engg, GB Pant University, India

*Corresponding Author
Email: [email protected]

Received Date:04 April 2022, Accepted Date:14 June 2022, Published Date:21 July 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Application of trend detection and fuzzy time series models to predict the growth of Uttarakhand state using night-Time Lights (NTL) dataset (Visible Infrared Imaging Radiometer Suite (VIIRS) night-time lights). Methods: In this work, we propose a methodology to analyze trends and estimate different fuzzy time series models for prediction in the context of forecasting the economic growth of a low-earning income Uttarakhand state. Mann- Kendall tests are used for performing trend analysis of Uttarakhand (India) using night-time luminosity data sets. Forecasting methods based on fuzzy time series models have been presented to deal with uncertainties induced by ambiguities, vagueness, and some other non-probabilistic factors, and have widely been employed in many problems domains. The simulation is done on the Jupyter Notebook platform using night-time luminosity datasets. Findings: We conclude that Uttarakhand and Himachal Pradesh show positive growth in trends for their night-time lights monthly average intensity with magnitude of 0.002182 and 0.002558 respectively. Night time luminosity database can be used as a proxy indicator for the economic growth of the Uttarakhand state as the correlation between NTL time series data of Uttarakhand and its Gross Development Product (GDP) is 0.795641021. Fuzzy time series models can also be used to predict the growth of the Uttarakhand state and the High Order Fuzzy Time Series model gives the accuracy metrics of root mean square (RMSE) as 0.11. Novelty: No previous research work is performed on trend analysis and prediction of growth of a Uttarakhand state using night-time luminosity data sets.

Keywords: Fuzzy Systems; Fuzzy Time Series; Prediction; Night time luminosity; Satellite image

References

  2. Pérez-Sindín XS, Chen THK, Prishchepov AV. Are night-time lights a good proxy of economic activity in rural areas in middle and low-income countries? Examining the empirical evidence from Colombia. Remote Sensing Applications: Society and Environment. 2021;24:100647. Available from: https://doi.org/10.1016/j.rsase.2021.100647
  3. Agnihotri J, Mishra S. Indian economy and Nighttime Lights. 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). 2021;p. 401–406. Available from: https://doi.org/10.1109/ICCIKE51210.2021.9410767
  4. Zheng Y, He Y, Zhou Q, Wang H. Quantitative Evaluation of Urban Expansion using NPP-VIIRS Nighttime Light and Landsat Spectral Data. Sustainable Cities and Society. 2022;76:103338. Available from: https://doi.org/10.1016/j.scs.2021.103338
  5. Mutanga O, Kumar L. Google Earth Engine Applications. Remote Sensing. 2019;11(5):591. Available from: https://doi.org/10.3390/rs11050591
  6. Hu Y, Yao J. Illuminating economic growth. Journal of Econometrics. 2022;228(2):359–378. Available from: https://doi.org/10.1016/j.jeconom.2021.05.007
  7. Uttarakhand G. 2022. Available from: https://uk.gov.in/
  8. Boechel T, Policarpo LM, Ramos GDO, Righi RDR. Fuzzy time series for predicting phenological stages of apple trees. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing. (pp. 934-941) ACM. 2021.
  9. Beeram SR, Kuchibhotla S. Time Series Analysis on Univariate and Multivariate Variables: A Comprehensive Survey. Communication Software and Networks. 2021;p. 119–126. Available from: https://doi.org/10.1007/978-981-15-5397-4_13
  10. Zhao M, Cheng C, Zhou Y, Li X, Shen S, Song C. A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights. Earth System Science Data. 2022;14(2):517–534. Available from: https://doi.org/10.5194/essd-14-517-2022
  11. Han G, Zhou T, Sun Y, Zhu S. The relationship between night-time light and socioeconomic factors in China and India. PLOS ONE. 2022;17(1):e0262503. Available from: https://doi.org/10.1371/journal.pone.0262503
  12. Hussain MD, Mahmud I. pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software. 2019;4(39):1556. Available from: https://doi.org/10.21105/joss.01556
  13. Gautam SS, Abhishekh. A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set. Journal of Control, Automation and Electrical Systems. 2019;30(4):532–544. Available from: https://doi.org/10.1007/s40313-019-00467-w

Copyright

© 2022 Verma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)

  • 28 July 2022

Year: 2022, Volume: 15, Issue: 28

Article Metrics


Post Graduate Fellowship in Research Communication

More articles

DON'T MISS OUT!

Subscribe now for latest articles and news.