Land Surface Temperature Changes in Songkhla, Thailand from 2001 to 2018

Rattikan Saelim; Salang Musikasuwan; Nasuha Chetae

doi:10.14456/nujst.2020.24

Authors

Rattikan Saelim Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani, 94000, Thailand , Centre of Excellence in Mathematics, CHE, Bangkok 10400, Thailand
Salang Musikasuwan Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani, 94000, Thailand, Centre of Excellence in Mathematics, CHE, Bangkok 10400, Thailand
Nasuha Chetae Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani, 94000, Thailand

DOI:

https://doi.org/10.14456/nujst.2020.24

Keywords:

Land Surface Temperature, temperature prediction, Remote Sensing, Linear Regression, ARIMA

Abstract

Agriculture is one of the most important factors contributing to the economy of Songkhla province in Thailand. Since the agriculture is highly dependent on the climate and hence on the temperatures, it was the aim of this study to investigate the trends and model the Land Surface Temperature (LST) from January 2001 to December 2018 of Songkhla province. Firstly, simple linear regression was applied and it was found that LST has increased approximately 0.3312 degrees Celsius during the last 18 years. After that the data were divided by 70:30 split to training and testing sets. Then for the predictive model, multiple linear regression and ARIMA (p,d,q) models were fit. Among the possible choices of (p,d,q) parameters in ARIMA, (3,0,0) performed the best. Further, according to their respective root mean squared errors (RMSE), namely 1.3334 and 1.3248, the ARIMA (3,0,0) performed slightly better than multiple linear regression in the training set. However, multiple linear regression performed slightly better than ARIMA (3,0,0) in the test data, with respective RMSEs 1.3249 and 1.3489. In other words, ARIMA gave a better fit, but linear regression gave better predictions. It is worth noting that the performance of a model type varies depending both on context and on the proportions of training and testing sets, so this case study demonstrates a model comparison approach but the results do not allow a generally applicable conclusion of ranking the model types

References

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, California: Pine Forge Press.

Bachelet, D., Brown, D., Böhm, M., & Russell, P. (1992). Climate change in Thailand and its potential impact on rice yield. Climatic change, 21(4), 347-366.

Ediger, V. Ş., Akar, S., & Uğurlu, B. (2006). Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model. Energy Policy, 34(18), 3836-3846.

Hassan, J. (2014). ARIMA and regression models for prediction of daily and monthly clearness index. Renewable Energy, 68, 421-427.

Hatfield, J. L., & Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather and climate extremes, 10, 4-10.

Iizumi, T., & Ramankutty, N. (2015). How do weather and climate influence cropping area and intensity? Global Food Security, 4, 46-50.

Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in natural Science, 19(12), 1665-1674.

Kinney Jr, W. R. (1978). ARIMA and regression in analytical review: An empirical test. Accounting Review, 51, 48-60.

Krämer, W. (1986). Least squares regression when the independent variable follows an ARIMA process. Journal of the American Statistical Association, 81(393), 150-154.

Limsakul, A., Kachenchart, B., Singhruck, P., Saramul, S., Santisirisomboon, J., & Apipattanavis, S. (2019). Updated basis knowledge of climate change summarized from the first part of Thailand’s Second Assessment Report on Climate Change. Applied Environmental Research, 41(2), 1-12.

Miswan, N. H., Said, R. M., & Anuar, S. H. H. (2016). ARIMA with regression model in modelling electricity load demand. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(12), 113-116.

Murat, M., Malinowska, I., Gos, M., & Krzyszczak, J. (2018). Forecasting daily meteorological time series using ARIMA and regression models. International agrophysics, 32(2), 253-264.

Office of Commercial Affairs Songkhla. (2018). Marketing Data in 2017. Thailand: Songkhla.

Parry, M. L., & Carter, T. R. (1989). An assessment of the effects of climatic change on agriculture. Climatic Change, 15(1-2), 95-116.

Parry, M. L., Rosenzweig, C., Iglesias, A., Livermore, M., & Fischer, G. (2004). Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Global environmental change, 14(1), 53-67.

Powell, J. P., & Reinhard, S. (2016). Measuring the effects of extreme weather events on yields. Weather and Climate extremes, 12, 69-79.

Sharma, D., & Babel, M. S. (2014). Trends in extreme rainfall and temperature indices in the western Thailand. International journal of Climatology, 34(7), 2393-2407.

Somparn, P., Gibb, M. J., Markvichitr, K., Chaiyabutr, N., Thummabood, S., & Vajrabukka, C. (2004). Analysis of climatic risk for cattle and buffalo production in northeast Thailand. International journal of biometeorology, 49(1), 59-64.

Stergiou, K. I. (1991). Short-term fisheries forecasting: comparison of smoothing, ARIMA and regression techniques. Journal of Applied Ichthyology, 7(4), 193-204.

Stergiou, K. I., Christou, E. D., & Petrakis, G. (1997). Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods. Fisheries Research, 29(1), 55-95.

Valizadeh, J., Ziaei, S. M., & Mazloumzadeh, S. M. (2014). Assessing climate change impacts on wheat production (a case study). Journal of the Saudi Society of Agricultural Sciences, 13(2), 107-115.

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S-PLUS (4th Ed.). New York: Springer-Verlag.