Simple Regression Model Analysis of the Effect of Temperature on Rainfall in Padang City Using Scikit-Learn
DOI:
https://doi.org/10.62671/jowim.v1i2.41Abstract
Temperature is one of the factors that influences the intensity of rainfall in an area. The higher the temperature in an area, the higher the evaporation so the greater the chance of rain, especially if this occurs at sea. The location of Padang City close to the sea influences the intensity of rainfall that occurs. Research was conducted to model the effect of temperature on rainfall that occurs in Padang City using simple linear regression using the Python programming language. The methodology used to model the relationship between these two variables is the Cross-Industry Standard Process for Data Mining. The simple regression model equation obtained is y = -26.73 x + 1119.98. The evaluation carried out on the model resulted in a relationship that was not close and had a negative
correlation. The error rate of the simple linear regression model used in this study is quite large with a mean model error value of 40%, an MAE value of 124.2311, MSE 23489.97 and RMSE 153.2644. A good model has MAE, MSE and RMSE close to zero. Further research is needed to find out a suitable model to describe the relationship between temperature and rainfall that occurs in Padang City.
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T. K. Manik and P. B. Timotiwu, “Tracing Global Warming Path in Local Scale: Greenhouse Gas Emission or Land Use Changes?,” Eur. J. Environ. Earth Sci., vol. 3, no. 6, pp. 69–74, 2022, doi: 10.24018/ejgeo.2022.3.6.357.
M. Ma, S. L. Collins, and G. Du, “Direct and indirect effects of temperature and precipitation on alpine seed banks in the Tibetan Plateau,” Ecol. Appl., vol. 30, no. 5, 2020, doi: 10.1002/eap.2096.
A. C. Review, “Flood Risk Management in Arid and Semi-Arid Areas :,” 2023.
K. L. Findell, P. W. Keys, R. J. van der Ent, B. R. Lintner, A. Berg, and J. P. Krasting, “Rising temperatures increase importance of oceanic evaporation as a source for continental precipitation,” J. Clim., vol. 32, no. 22, pp. 7713–7726, 2019, doi: 10.1175/JCLI-D-19-0145.1.
H. A. Musyayyadah and M. Vonnisa, “Analisa Pola Temperatur Udara Permukaan di Sumatera Barat Tahun 1980 - 2017,” J. Fis. Unand, vol. 8, no. 1, pp. 91–97, 2019, doi: 10.25077/jfu.8.1.91-97.2019.
R. Alokozay, “Impact of Climate Change on Water Resources in Kabul City,” Int. Res. J. Eng. Technol., no. May, pp. 1679–1688, 2020, [Online]. Available: www.irjet.net
N. Thanh Son, H. Le Huong, T. Thi Phuong, and T. Trong Phuong, “Assessing Change Impacts on Flood and Drought hazard in Thua Thien Hue province using Standardized Precipitation Index (SPI),” IOP Conf. Ser. Earth Environ. Sci., vol. 1345, no. 1, p. 012022, 2024, doi: 10.1088/1755-1315/1345/1/012022.
S. Eka Putri, A. F. Corp, Rembrandt, Dasman Lanin, Genius Umar, and Mulya Gusman, “Kota Padang : Identifikasi Potensi Bencana Banjir Dan Upaya Mitigasi,” J. Ilm. Multidisiplin Nusant., vol. 1, no. 3, pp. 116–122, 2023, doi: 10.59435/jimnu.v1i3.56.
W. Prarikeslan et al., “The Impact of Climate Change on the City of Padang, Indonesia,” Nat. Environ. Pollut. Technol., vol. 22, no. 4, pp. 2223–2229, 2023, doi: 10.46488/NEPT.2023.v22i04.050.
A. Y. Nofrizal, H. Rahman, and M. Hanif, “Prediction of Seawater Flooding Hazard on Settlement Areas in Padang City as a Climate Change Impact using GIS and Remote Sensing Technology,” IOP Conf. Ser. Earth Environ. Sci., vol. 303, no. 1, 2019, doi: 10.1088/1755-1315/303/1/012025.
S. Fawzy, A. I. Osman, J. Doran, and D. W. Rooney, “Strategies for mitigation of climate change: a review,” Environ. Chem. Lett., vol. 18, no. 6, pp. 2069–2094, 2020, doi: 10.1007/s10311-020-01059-w.
K. Abbass, M. Z. Qasim, H. Song, M. Murshed, H. Mahmood, and I. Younis, “A review of the global climate change impacts, adaptation, and sustainable mitigation measures,” Environ. Sci. Pollut. Res., vol. 29, no. 28, pp. 42539–42559, 2022, doi: 10.1007/s11356-022-19718-6.
M. Mudelsee, “Trend analysis of climate time series: A review of methods,” Earth-Science Rev., vol. 190, no. June 2018, pp. 310–322, 2019, doi: 10.1016/j.earscirev.2018.12.005.
N. Nelvidawati and M. Kasman, “Penggunaan Korelasi Spearman Untuk Menguji Hubungan Suhu Dan Besarnya Curah Hujan Bulanan di Kota Padang”.
C. Schröer, F. Kruse, and J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia Comput. Sci., vol. 181, no. 2019, pp. 526–534, 2021, doi: 10.1016/j.procs.2021.01.199.
N. Lebkiri et al., “Using Machine Learning for Prediction Students Failure in Morocco: an Application of the CRISP-DM Methodology,” Int. J. Educ. Inf. Technol., vol. 15, pp. 344–352, 2021, doi: 10.46300/9109.2021.15.36.
Aviral Gupta, Akshay Sharma, and Dr. Amita Goel, “Review of Regression Analysis Models,” Int. J. Eng. Res., vol. V6, no. 08, pp. 58–61, 2017, doi: 10.17577/ijertv6is080060.
D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 1, no. 2, pp. 140–147, 2020, doi: 10.38094/jastt1457.
T. Esaki, “Appropriate evaluation measurements for regression models,” Chem-Bio Informatics J., vol. 21, pp. 59–69, 2021, doi: 10.1273/cbij.21.59.
F. Emmert-Streib and M. Dehmer, “Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error,” Mach. Learn. Knowl. Extr., vol. 1, no. 1, pp. 521–551, 2019, doi: 10.3390/make1010032.
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