Forecasting the Monthly Temperature and Rainfall in Chuping Perlis
DOI:
https://doi.org/10.37134/ejsmt.vol11.2.3.2024Keywords:
Forecasting, Simple Seasonal Exponential Smoothing (SSES), Holt-Winter Additive, Holt-Winter Multiplicative, Seasonal ARIMAAbstract
This study focuses on the forecasting of monthly temperature and rainfall patterns in Chuping, Perlis, with the aim of providing valuable insights into the region's climate. Various forecasting methods were employed, including Simple Seasonal Exponential Smoothing (SSES), Holt Winter Additive, Holt Winter Multiplicative, and Seasonal ARIMA. The accuracy of these models was assessed using key error metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results of the analysis revealed that the Simple Seasonal Exponential Smoothing (SSES) model consistently outperformed the other methods, exhibiting the lowest error metrics for both temperature and rainfall forecasts. Specifically, for monthly temperature, the lowest error metrics values were found to be 0.401 for MAE, 0.465 for RMSE, and 1.434 for MAPE. For monthly rainfall, the SSES model demonstrated that the MAE of 1.528, RMSE of 1.952, and MAPE of 157.477, indicating its superior accuracy in capturing the seasonal patterns in Chuping's climate. The study's conclusions indicate a predictable climate in Chuping, with stable temperature and rainfall patterns expected over the next 31 months, until the end of 2025. This reliability in forecasting provides valuable information for various sectors, including agriculture and environmental management, which rely on accurate climate predictions for planning and resource allocation.
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References
Wen, Y. W. J., Ponnusamy, R. R., & Kang, H. M. (2019). Application of weather index-based insurance for paddy yield: The case of Malaysia. Int. J. Adv. Appl. Sci, 6, 51-59.
Md Nor, S. M., Wan Hussin, W. M. R., & Ab Latif, Z. (2019). Impacts of rainfall patterns on paddy productivity in Perlis, Malaysia. Environmental Science and Pollution Research, 26(12), 12429-12440.
Laman Web Rasmi Jabatan Meteorologi Malaysia. (2020). Met.gov.my. https://www.met.gov.my/en/penerbitan/laporan-tahunan/
Yatim, A. N. M., Latif, M. T., Ahamad, F., Khan, M. F., Nadzir, M. S. M., & Juneng, L. (2019). Observed trends in extreme temperature over the Klang Valley, Malaysia. Advances in Atmospheric Sciences, 36, 1355-1370.
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E., & Peng, B. (2019). Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biology.
Iseh, A. J., & Woma, T. Y. (2013). Weather forecasting models, methods and applications. Int. J. Eng. Res. Technol, 2, 1945-1956.
Ongoma, V. (2022, February). The science of weather forecasting: what it takes and why it’s so hard to get right. The Conversation. https://theconversation.com/the-science-of-weather-forecasting-what-it-takes-and-why-its-so-hard-to-get-right-175740#:~:text=Weather%20forecasting%20is%20an%20important,vital%20in%20the%20coming%20years.
Hoey, B. (2020, October 29). The Benefits of Forecasting in Transportation Planning. Flexis.com; flexis Information Systems Inc.
Merz, B., Kuhlicke, C., Kunz, M., Pittore, M., Babeyko, A., Bresch, D. N., … Wurpts, A. (2020). Impact Forecasting to Support Emergency Management of Natural Hazards. Reviews of Geophysics.
Shivhare, N., Rahul, A. K., Dwivedi, S. B., & Dikshit, P. K. S. (2019). ARIMA based daily weather forecasting tool: A case study for Varanasi. Mausam, 70(1), 133-140.
Natayu, A., Clarke, Q. J. H., & Fikri, M. (2022). Benchmark of Holt-Winters and SARIMA Methods in Predicting Jakarta Climate.
Murat M, Malinowska I, Hoffmann H and Baranowski P. (2016). Statistical modelling of agrometeorological time series by exponential smoothing. International Agrophysics 30: 57–65.
Heydari, M., Ghadim, H. B., Rashidi, M., & Noori, M. (2020). Application of holt-winters time series models for predicting climatic parameters (case study: Robat Garah-Bil Station, Iran). Pol J Environ Stud, 29(1), 617-627.
Nurzawanah Raihah Zamri, & Azmi, N. (2021). Global Warming in Cameron Highlands: Forecasting its Temperature Level via ARIMA vs ARAR. 2084(1), 012009–012009.
Hossen, S. M., Ismail, M. T., & tabash, m. I. (2021). The impact of seasonality in temperature forecast on tourist arrivals in bangladesh: an empirical evidence. GeoJournal of Tourism and Geosites, 34(1), 20–27.
Xhabafti, M., & Sinaj, v. (2022). Weather forecasting based on the application of SARIMA models. Circular Economy, 549.
Amjad, M., Khan, A., Fatima, K., Ajaz, O., Ali, S., & Main, K. (2022). Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models. Atmosphere, 14(1), 88.
Saba Zafar, M. K., Khan, M. I., & Nida, H. (2020). Application of simple exponential smoothing method for temperature forecasting in two major cities of the Punjab, Pakistan.
Oxford Learner’s Dictionaries | Find definitions, translations, and grammar explanations at Oxford Learner’s Dictionaries. (2023). Oxfordlearnersdictionaries.com. https://www.oxfordlearnersdictionaries.com/
Precipitation and the Water Cycle | U.S. Geological Survey. (2019). Usgs.gov. https://www.usgs.gov/special-topics/water-science-school/science/precipitation-and-water-cycle#:~:text=Rain%20and%20snow%20are%20key,drinks%20to%20plants%20and%20animals.
Mohammed, M., Kolapalli, R., Golla, N., & Maturi, S. (2020). Prediction of Rainfall Using Machine Learning Techniques. http://www.ijstr.org/final-print/jan2020/Prediction-Of-Rainfall-Using-Machine-Learning-Techniques.pdf
Swain, S., Nandi, S., & Patel, P. (2018). Development of an ARIMA Model for Monthly Rainfall Forecasting over Khordha District, Odisha, India. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 2, 708, 325.
Pertiwi, D. D. (2020). Applied Exponential SMoothing Holt-Winter Method for Rainfall Forecast in Mataram City. J Int Comp & He Inf, 1(2).
Wiguna, I. K. A. G., Utami, N. L. P. A. C., Parwita, W. G. S., Udayana, I. P. A. E. D., & Sudipa, I. G. I. (2023). Rainfall Forecasting Using the Holt-Winters Exponential Smoothing Method. Jurnal Info Sains: Informatika dan Sains, 13(01), 15-23.
Olaofe, ZO, 2015. Wind energy predictions of small-scale turbine output using exponential smoothing and feed-forward neural network. International Journal of Energy Engineering 5: 28-42.
Siregar B, Butar-Butar IA., Rahmat RF, Andayani U and Fahmi F, 2017. Comparison of exponential smoothing methods in forecasting palm oil real production. Journal of Physics: Conference Series 801: Article # 0120041.
Box G.E.P. and Jenkins G., 1970. Time Series Analysis: forecasting and control. San Francisco, Holden-Day.
Box G.E.P. and Tiao G.C., 1975. Intervention Analysis with Applications to Economic and Environmental Problems. JASA, 70, 70-79
Siami-Namini, S., Tavakoli, N., Siami Namin, A., (2018). A Comparison of ARIMA and LSTM in Forecasting Time Series, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Presented at the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, Orlando, FL, pp. 1394–1401.
Farida, M., Zeghdoudi, H., (2020). On Modelling seasonal ARIMA series: Comparison, Application and Forecast (Number of Injured in Road Accidents in Northeast Algeria). WSEAS
Trans. Syst. Control. 15, 235–246.
Mantalos, P., Mattheou, K., Karagrigoriou, A., (2010). Forecasting ARMA models: a comparative study of information criteria focusing on MDIC. J. Stat. Comput. Simul. 80, 61–73.
Marera DHSV, 2016. An application of exponential smoothing methods to weather related data. MS Thesis, School of Statistics and Actuarial Science, Faculty of Science.
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