Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang

  • Isman Kurniawan Telkom University
  • Lusi Sofiana Silaban Telkom University
  • Devi Munandar Indonesian Institute of Sciences
Keywords: time series, temperature, CNN, MLP, hybrid CNN-MLP


Weather prediction is usually performed for a reference in planning future activity. The prediction is performed by considering several parameters, such as temperature, air pressure, humidity, wind, rainfall, and others. In this study, the temperature, as one of weather parameters, is predicted by using time series from January 2015 to December 2017. The data was obtained from Lembaga Ilmu Pengetahuan Indonesia (LIPI) weather measurement station in Muaro Anai, Padang. The predictions were carried out by using Convolutional Neural Network (CNN), Multilayer  Perceptron  (MLP), and the hybrid of CNN-MLP methods. The parameters used in the CNN method, such as the number of filters and kernel size, and used in the MLP method, such as the number of hidden layers and number of neurons, were selected by performing the hyperparameter tuning procedure. After obtaining the best parameters for both methods, the performance of both methods was evaluated by calculating the value of Root Mean Square Error (RMSE) and R2. Based on the results, we found that the prediction by CNN is more accurate than other method. This is indicated by the highest value of R2 of the prediction obtained by CNN method.


Download data is not yet available.


S. Khan, M. Muqeem, and N. Javed, “A Critical Review of Data mining Techniques in Weather Forecasting,” IJARCCE, vol. 5, 2016.

Ö. Altan Dombaycı and M. Gölcü, “Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey,” Renewable Energy, vol. 34, no. 4, pp. 1158–1161, Apr. 2009, doi: 10.1016/j.renene.2008.07.007.

N. R. Chithra, S. G. Thampi, S. Surapaneni, R. Nannapaneni, A. A. K. Reddy, and J. D. Kumar, “Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models,” Theor Appl Climatol, vol. 121, no. 3, pp. 581–590, Aug. 2015, doi: 10.1007/s00704-014-1257-1.

T. Appelhans, E. Mwangomo, D. R. Hardy, A. Hemp, and T. Nauss, “Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania,” Spatial Statistics, vol. 14, pp. 91–113, Nov. 2015, doi: 10.1016/j.spasta.2015.05.008.

I. Tasadduq, S. Rehman, and K. Bubshait, “Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia,” Renewable Energy, vol. 25, no. 4, pp. 545–554, Apr. 2002, doi: 10.1016/S0960-1481(01)00082-9.

B. A. Smith, G. Hoogenboom, and R. W. McClendon, “Artificial neural networks for automated year-round temperature prediction,” Computers and Electronics in Agriculture, vol. 68, no. 1, pp. 52–61, Aug. 2009, doi: 10.1016/j.compag.2009.04.003.

B. Ustaoglu, H. K. Cigizoglu, and M. Karaca, “Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods,” Meteorological Applications, vol. 15, no. 4, pp. 431–445, 2008, doi: 10.1002/met.83.

A. Paniagua-Tineo, S. Salcedo-Sanz, C. Casanova-Mateo, E. G. Ortiz-García, M. A. Cony, and E. Hernández-Martín, “Prediction of daily maximum temperature using a support vector regression algorithm,” Renewable Energy, vol. 36, no. 11, pp. 3054–3060, Nov. 2011, doi: 10.1016/j.renene.2011.03.030.

A. Mellit, A. M. Pavan, and M. Benghanem, “Least squares support vector machine for short-term prediction of meteorological time series,” Theor Appl Climatol, vol. 111, no. 1, pp. 297–307, Jan. 2013, doi: 10.1007/s00704-012-0661-7.

E. G. Ortiz-García, S. Salcedo-Sanz, C. Casanova-Mateo, A. Paniagua-Tineo, and J. A. Portilla-Figueras, “Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks,” Atmospheric Research, vol. 107, pp. 1–8, Apr. 2012, doi: 10.1016/j.atmosres.2011.10.013.

R. F. Chevalier, G. Hoogenboom, R. W. McClendon, and J. A. Paz, “Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks,” Neural Comput & Applic, vol. 20, no. 1, pp. 151–159, Feb. 2011, doi: 10.1007/s00521-010-0363-y.

F. Sharifzadeh, G. Akbarizadeh, and Y. Seifi Kavian, “Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier,” J Indian Soc Remote Sens, vol. 47, no. 4, pp. 551–562, Apr. 2019, doi: 10.1007/s12524-018-0891-y.

C. Zhang et al., “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 140, pp. 133–144, Jun. 2018, doi: 10.1016/j.isprsjprs.2017.07.014.

Y. Chu, C. Huang, X. Xie, B. Tan, S. Kamal, and X. Xiong, “Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling,” Computational Intelligence and Neuroscience, Nov. 01, 2018. (accessed Oct. 09, 2020).

D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” J. Physiol. Paris, vol. 195, pp. 215–243, 1968.

J. Wu, “Introduction to convolutional neural networks,” National Key Lab for Novel Software Technology, Nanjing University, China: Semantic Scholar, 2017, pp. 5–23.

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 International Conference on Engineering and Technology (ICET), Aug. 2017, pp. 1–6, doi: 10.1109/ICEngTechnol.2017.8308186.

T. Guo, J. Dong, H. Li, and Y. Gao, “Simple convolutional neural network on image classification,” in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Mar. 2017, pp. 721–724, doi: 10.1109/ICBDA.2017.8078730.

T. Perol, M. Gharbi, and M. Denolle, “Convolutional neural network for earthquake detection and location,” Science Advances, vol. 4, no. 2, p. e1700578, Feb. 2018, doi: 10.1126/sciadv.1700578.

K. Gurney, An introduction to neural networks. CRC press, 2014.

M. Riedmiller, “Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms,” Comput. Stand. Inter., vol. 16, pp. 265–278, 1994.

S. Haykin, Neural networks: a comprehensive foundation. Prentice Hall PTR, 1994.

A. Marchisio, M. A. Hanif, S. Rehman, M. Martina, and M. Shafique, “A methodology for automatic selection of activation functions to design hybrid deep neural networks,” 2018.

R. F. MELLO and M. A. Ponti, Machine Learning: A Practical Approach on the Statistical Learning Theory. Springer, 2018.

H. Ramchoun, Y. Ghanou, M. Ettaouil, and M. A. J. Idrissi, “Multilayer Perceptron: Architecture Optimization and Training,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. Special Issue on Artificial Intelligence Underpinning, 2016, Accessed: Oct. 09, 2020. [Online]. Available:

H. Ramchoun, M. A. J. Idrissi, Y. Ghanou, and M. Ettaouil, “Multilayer Perceptron: Architecture Optimization and training with mixed activation functions,” in Proceedings of the 2nd international Conference on Big Data, Cloud and Applications, New York, NY, USA, Mar. 2017, pp. 1–6, doi: 10.1145/3090354.3090427.

T. Chai and R. R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature,” Geosci. Model Dev., vol. 7, pp. 1247–1250, 2014.

S. Lee, Y.-S. Lee, and Y. Son, “Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks,” Applied Sciences, vol. 10, no. 5, Art. no. 5, Jan. 2020, doi: 10.3390/app10051609.

How to Cite
Kurniawan, I., Silaban, L. S., & Munandar, D. (2020). Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(6), 1165 -.
Artikel Teknologi Informasi