Comparative Analysis of LSTM, BILSTM and ARIMA for Time Series Forecasting on 116 years of Temperature and Rainfall Data from Pakistan

Authors

  • Asif Khokhar  Department of Software Engineering, Mehran University of Engineering and technology Jamshoro Sindh, Pakistan
  • Shahnawaz Talpur  Department of Computer System Engineering, Mehran University of Engineering and technology Jamshoro Sindh, Pakistan
  • Mohsin A. Memon  Department of Software Engineering, Mehran University of Engineering and technology Jamshoro Sindh, Pakistan

DOI:

https://doi.org//10.32628/CSEIT2390238

Keywords:

LSTM, BILSTM, ARIMA, Rain Forecasting and Temperature Forecasting.

Abstract

Numerous aspects of human life, including agriculture, transportation, and health, are significantly influenced by weather, both economically and socially. Rain has an impact on landslides, floods, and other natural disasters. We are motivated to create a model for comprehending and forecasting rain in order to provide advanced warning in a spectrum of areas such as transport, agriculture, and so on because of the numerous consequences that rain and temperature have on human survival. In this study, a dataset for temperature and rainfall for Pakistan for 116 years is used. Comparative analysis of ARIMA, LSTM and BILSTM is performed. For this study, 90% of data is used for training and the 10% for testing. Normalization is also performed to clean data. According to the results, LSTM and BILSTM are better than ARIMA but for specific cases of rainfall, BILSTM performed better than LSTM and for Temperature LSTM outperformed BILSTM.

References

  1. Abbas, Sohail, and Zulfiqar Ali Mayo. "Impact of temperature and rainfall on rice production in Punjab, Pakistan." Environment, Development and Sustainability 23.2 (2021): 1706-1728.
  2. Imran, Muhammad Ali, et al. "Impact of Climate Smart Agriculture (CSA) practices on cotton production and livelihood of farmers in Punjab, Pakistan." Sustainability 10.6 (2018): 2101.
  3. Siddiqui, R., Samad, G., Nasir, M., & Jalil, H. H. (2012). The impact of climate change on major agricultural crops: evidence from Punjab, Pakistan. The Pakistan Development Review, 261–274.
  4. Hanif, U., Syed, S. H., Ahmad, R., Malik, K. A., & Nasir, M. (2010). Economic impact of climate change on the agricultural sector of Punjab [with comments]. The Pakistan Development Review, 49, 771–798.
  5. Ali, Ghaffar, et al. "Spatial–temporal characterization of rainfall in Pakistan during the past half-century (1961–2020)." Scientific reports 11.1 (2021): 1-15.
  6. Syed, F.S., Kucharski, F.: Statistically Related Coupled Modes of South Asian Summer Monsoon Interannual Variability in the Tropics. Atmos. Sci. Lett. 17(2), 183–189 (2016)
  7. Kucharski, Fred and Muhammad Adnan Abid. 2017. “Interannual Variability of the Indian Monsoon and Its Link to ENSO.” Oxf. Res. Encycl. Clim. Sci. (November):1–24
  8. Adnan, M., Rehman, N., Ali, S., Mehmood, S., Mir, K.A., Khan, A.A., Khalid, B.: Prediction of Summer Rainfall in Pakistan from Global Sea-Surface Temperature and Sea-Level Pressure. Weather. 72(3), 76–84 (2017)
  9. Mahessar, Ali Asghar, et al. "Rainfall Analysis for Hyderabad and Nawabshah, Sindh, Pakistan." Engineering, Technology & Applied Science Research 10.6 (2020): 6597-6602.
  10. Falzon, Greg, et al. "ClassifyMe: a field-scouting software for the identification of wildlife in camera trap images." Animals 10.1 (2020): 58.
  11. Hattermann, F.F.; Krysanova, V.; Gosling, S.; Dankers, R.; Daggupati, P.; Donnelly, C.h.; Flörke, M.; Huang, S.; Motovilov, Yu.; Buda, S.; et al. Cross-scale inter-comparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins. Clim. Change 2017, 141, 561–576.
  12. Yaqoob, Nusrat, et al. "The effects of agriculture productivity, land intensification, on sustainable economic growth: a panel analysis from Bangladesh, India, and Pakistan Economies." Environmental Science and Pollution Research (2022): 1-9.
  13. Singh, Nitin, Saurabh Chaturvedi, and Shamim Akhter. "Weather forecasting using machine learning algorithm." 2019 International Conference on Signal Processing and Communication (ICSC). IEEE, 2019.
  14. Anjali, T., et al. "Temperature prediction using machine learning approaches." 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). Vol. 1. IEEE, 2019.
  15. Usmani, Z.-ul-hassan. Pakistan temperature. Kaggle. Retrieved March 1, 2023, from https://www.kaggle.com/datasets/zusmani/pakistan-temperature (2021, March 19)
  16. Usmani, Z.-ul-hassan. (2021, March 13). Rainfall in Pakistan. Kaggle. Retrieved March 1, 2023, from https://www.kaggle.com/datasets/zusmani/rainfall-in-pakistan
  17. Van Houdt, Greg, Carlos Mosquera, and Gonzalo Nápoles. "A review on the long short-term memory model." Artificial Intelligence Review 53.8 (2020): 5929-5955.
  18. Alawneh, Luay, et al. "A comparison of unidirectional and bidirectional lstm networks for human activity recognition." 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2020.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Asif Khokhar, Shahnawaz Talpur, Mohsin A. Memon, " Comparative Analysis of LSTM, BILSTM and ARIMA for Time Series Forecasting on 116 years of Temperature and Rainfall Data from Pakistan, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.350-357, March-April-2023. Available at doi : https://doi.org/10.32628/CSEIT2390238