Forecasting of Giresun Temperature and Pressure Data with Multilayer Artificial Neural Network

Year 2026, Volume: 10 Issue: 1, 1 - 7, 12.03.2026

Emine Kölemen , Özlem Karahasan

https://doi.org/10.34110/forecasting.1811454

https://izlik.org/JA35WN67BC

Abstract

In recent years, artificial neural networks have gained significant popularity for addressing time series forecasting challenges. While traditional forecasting techniques are still widely used, they generally perform well on linear datasets. However, many real-world time series exhibit nonlinear and complex behaviours, which limits the effectiveness of classical models. To overcome this limitation, deep neural network architecture such as multilayer perceptions artificial neural network offers a promising alternative due to their capacity for capturing nonlinear patterns and modelling complex relationships through a larger number of parameters. This study focuses on forecasting weather-related variables using a multilayer perceptions artificial neural network, utilizing atmospheric pressure and temperature data collected from Giresun, Turkey. The primary goal is to identify seasonal trends and forecasting future values of these weather indicators. The performance of the proposed MLP model is evaluated and compared with several commonly adopted neural network-based approaches reported in the literature.

Keywords

Multilayer Perceptron , Giresun Temperature and Pressure Data , Forecasting

References

• [1] Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice hall PTR.
• [2] Panigrahi, S., & Behera, H. S. (2017). A hybrid ETS–ANN model for time series forecasting. Engineering applications of artificial intelligence, 66, 49-59.
• [3] Qing, X., & Niu, Y. (2018). Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy, 148, 461-468.
• [4] Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151-163.
• [5] Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical mechanics and its applications, 519, 127-139.
• [6] Shen, Z., Zhang, Y., Lu, J., Xu, J., & Xiao, G. (2020). A novel time series forecasting model with deep learning. Neurocomputing, 396, 302-313.
• [7] Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F., & Liu, Y. (2020). Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station. Soft Computing, 24(21), 16453-16482.
• [8] Sharadga, H., Hajimirza, S., & Balog, R. S. (2020). Time series forecasting of solar power generation for large-scale photovoltaic plants. Renewable Energy, 150, 797-807.
• [9] Dubey, A. K., Kumar, A., García-Díaz, V., Sharma, A. K., & Kanhaiya, K. (2021). Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments, 47, 101474.
• [10] Hanoon, M. S., Ahmed, A. N., Zaini, N. A., Razzaq, A., Kumar, P., Sherif, M., ... & El-Shafie, A. (2021). Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia. Scientific Reports, 11(1), 18935.
• [11] Niu, W. J., & Feng, Z. K. (2021). Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management. Sustainable Cities and Society, 64, 102562.
• [12] Farsi, M., Hosahalli, D., Manjunatha, B. R., Gad, I., Atlam, E. S., Ahmed, A., ... & Ghoneim, O. A. (2021). Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data. Alexandria Engineering Journal, 60(1), 1299-1316.
• [13] Wei, X., Zhang, L., Yang, H. Q., Zhang, L., & Yao, Y. P. (2021). Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks. Geoscience Frontiers, 12(1), 453-467.
• [14] Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204.
• [15] Ensafi, Y., Amin, S. H., Zhang, G., & Shah, B. (2022). Time-series forecasting of seasonal items sales using machine learning–A comparative analysis. International Journal of Information Management Data Insights, 2(1), 100058.
• [16] Xu, Y., Hu, C., Wu, Q., Jian, S., Li, Z., Chen, Y., ... & Wang, S. (2022). Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation. Journal of hydrology, 608, 127553.
• [17] Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557.
• [18] Guo, Q., He, Z., & Wang, Z. (2023). Prediction of hourly PM2. 5 and PM10 concentrations in Chongqing City in China based on artificial neural network. Aerosol and Air Quality Research, 23(6), 220448.
• [19] Özdoğan-Sarıkoç, G., Sarıkoç, M., Celik, M., & Dadaser-Celik, F. (2023). Reservoir volume forecasting using artificial intelligence-based models: artificial neural networks, support vector regression, and long short-term memory. Journal of Hydrology, 616, 128766.
• [20] Pavlatos, C., Makris, E., Fotis, G., Vita, V., & Mladenov, V. (2023). Enhancing electrical load prediction using a bidirectional LSTM neural network. Electronics, 12(22), 4652.
• [21] Menéndez-García, L. A., García-Nieto, P. J., García-Gonzalo, E., & Lasheras, F. S. (2024). Time series analysis for COMEX platinum spot price forecasting using SVM, MARS, MLP, VARMA and ARIMA models: A case study. Resources Policy, 95, 105148.
• [22] Hua, Q., Fan, Z., Mu, W., Cui, J., Xing, R., Liu, H., & Gao, J. (2024). A short-term power load forecasting method using CNN-GRU with an attention mechanism. Energies, 18(1), 106.
• [23] Mohammadi, M., Jamshidi, S., Rezvanian, A., Gheisari, M., & Kumar, A. (2024). Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation. Measurement: Sensors, 33, 101179.
• [24] Maleki, N., Lundström, O., Musaddiq, A., Jeansson, J., Olsson, T., & Ahlgren, F. (2024). Future energy insights: Time-series and deep learning models for city load forecasting. Applied Energy, 374, 124067.
• [25] Wang, H., Mo, Y., Dai, H., Yin, N., Fan, S., & Li, B. (2024). FTMLP: MLP with Feature-Temporal Block for multivariate time series forecasting. Neurocomputing, 607, 128365.
• [26] Su, J., Lin, Z., Xu, F., Fathi, G., & Alnowibet, K. A. (2024). A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality. Scientific Reports, 14(1), 23927.
• [27] Lazcano, A., Jaramillo-Morán, M. A., & Sandubete, J. E. (2024). Back to basics: The power of the multilayer perceptron in financial time series forecasting. Mathematics, 12(12), 1920.
• [28] Ciechulski, T., & Osowski, S. (2024). Wind power short-term time-series prediction using an ensemble of neural networks. Energies, 17(1), 264.
• [29] Wang, Z., Kong, F., Feng, S., Wang, M., Yang, X., Zhao, H., ... & Zhang, Y. (2025). Is mamba effective for time series forecasting? Neurocomputing, 619, 129178.
• [30] Bilgili, M., Pinar, E., & Durhasan, T. (2025). Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models. Earth Science Informatics, 18(1), 10.
• [31] Aljuaydi, F., Zidan, M., & Elshewey, A. M. (2025). A Deep Learning CNN-GRU-RNN Model for Sustainable Development Prediction in Al-Kharj City. Engineering, Technology & Applied Science Research, 15(1), 20321-20327.
• [32] Uluocak, I. (2025). Comparative study of multivariate hybrid neural networks for global sea level prediction through 2050. Environmental Earth Sciences, 84(3), 79.
• [33] Liu, Y., Saleem, M. S., Rashid, J., Ahmad, S., & Faheem, M. (2025). Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods. Energy Science & Engineering, 13(1), 119-139.
• [34] Lu, L., Yang, T., Chen, Z., Ge, Q., Yang, J., & Sen, G. (2025). Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series. Scientific Reports, 15(1), 1232.
• [35] Coutinho, E. R., Madeira, J. G., Borges, D. G., Springer, M. V., de Oliveira, E. M., & Coutinho, A. L. (2025). Multi-step forecasting of meteorological time series using CNN-LSTM with decomposition methods. Water Resources Management, 1-26.
• [36] Jiang, H., Liu, D., Ding, X., Chen, Y., & Li, H. (2025). TCM: An efficient lightweight MLP-based network with affine transformation for long-term time series forecasting. Neurocomputing, 617, 128960.
• [37] Kermanshahi, B., & Iwamiya, H. (2002). Up to year 2020 load forecasting using neural nets. International Journal of Electrical Power & Energy Systems, 24(9), 789-797.
• [38] Khozani, Z. S., Banadkooki, F. B., Ehteram, M., Ahmed, A. N., & El-Shafie, A. (2022). Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level. Journal of Cleaner Production, 348, 131224.
• [39] Golden, R. M., & Rumelhart, D. E. (1993). A parallel distributed processing model of story comprehension and recall. Discourse processes, 16(3), 203-237.
• [40] Yadav, R. N., Kalra, P. K., & John, J. (2007). Time series prediction with single multiplicative neuron model. Applied soft computing, 7(4), 1157-1163.
• [41] Shin, Y., & Ghosh, J. (1991, July). The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation. In IJCNN-91-Seattle international joint conference on neural networks (Vol. 1, pp. 13-18). IEEE.