Performance Evaluation of Machine Learning Models for Weather Forecasting

Iliyas Ibrahim Iliyas

¹ Department of Mathematic Science, University of Maiduguri, Borno State, Nigeria

² Department of General Studies Education, Federal College of Education, Yola, Adamawa State, Nigeria

* Corresponding author’s E-mail: [email protected] 

Temperature is used to indicate variability and climate changes that indicate the process which is been carried out within the ecosystem and its services. The lack of knowledge about temperature affects human lives in terms of agriculture, transportation, mining, etc. temperature forecasting is used to predict atmospheric conditions based on parameters that caused the temperature to change. This study aims to explore the use of machine learning models for the prediction of temperature, evaluate the performance of these models, and use the model to predict temperature. In this study we explore the use of four different machine learning algorithms for forecasting weather temperature, the algorithms are: Ridge, Random Forest, Linear Regression, and Decision tree. We divided the dataset into training and testing sets, The models were tested on 1000 testing sets based on RMSE score with Decision Tree having the best score of 0.036, Random Forest: 0.208 while Logistic Regression and Ridge had the lowest score of 0.759 respectively. 

Regression, Atmosphere, Models, Forecasting, Temperature

Iliyas Ibrahim Iliyas, Andra Umoru, Adati Elkanah Chahari, Mustapha Mallam Garba (2022). Performance Evaluation of Machine Learning Models for Weather Forecasting. Journal of Artificial Intelligence and Systems, 4, 22–32. https://doi.org/10.33969/AIS.2022040102.

[1] Athiyarath, S., Paul, M., & Krishnaswamy, S. (2020). A comparative study and analysis of time series forecasting techniques. SN Computer Science, 1(3), 1-7. https://doi.org/10.1007/s42979-020-00180-5

[2] Cramer, S., Kampouridis, M., Freitas, A. A., & Alexandridis, A. K. (2017). An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Systems with Applications, 85, 169-181. https://doi.org/10.1016/j.eswa.2017.05.029

[3] Deepa, N., Prabadevi, B., Maddikunta, P. K., Gadekallu, T. R., Baker, T., Khan, M. A., & Tariq, U. (2021). An AI-based intelligent system for healthcare analysis using Ridge-Adaline Stochastic Gradient Descent Classifier. The Journal of Supercomputing, 77(2), 1998-2017. https://doi.org/10.1007/s11227-020-03347-2

[4] Graf, R., Zhu, S., & Sivakumar, B. (2019). Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. Journal of Hydrology, 578, 124115. https://doi.org/10.1016/j.jhydrol.2019.124115

[5] 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. https://doi.org/10.1007/s00500-020-04954-0

[6] Holmstrom, M., Liu, D., & Vo, C. (2016). Machine learning applied to weather forecasting. Meteorol. Appl, 1-5.

[7] Jakaria, A. H. M., Hossain, M. M., & Rahman, M. A. (2020). Smart weather forecasting using machine learning: a case study in Tennessee. arXiv preprint arXiv:2008.10789. https://doi.org/10.48550/arXiv.2008.10789

[8] Jaseena, K. U., & Kovoor, B. C. (2020). Deterministic weather forecasting models based on intelligent predictors: A survey. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.09.009

[9] Nishat, M. M., Dip, R. R., Faisal, F., Nasrullah, S. M., Ahsan, R., Shikder, M. F., & Hoque, M. A. (2021). A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms. EAI Endorsed Transactions on Pervasive Health and Technology, 18(e6). https://doi.org/10.4108/eai.13-8-2021.170671

[10] Ray, S. (2019). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (35-39). IEEE. doi: 10.1109/COMITCon.2019.8862451

[11] Raza, K. & Jothiprakash, V. (2014). Multi-Output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station. Journal of The Institution of Engineers (India): Series A, Springer, 95(4): 221-229. http://doi.org/10.1007/s40030-014-0092-9

[12] Sharma, N., Sharma, P., Irwin, D., & Shenoy, P. (2011). Predicting solar generation from weather forecasts using machine learning. In 2011 IEEE international conference on smart grid communications (SmartGridComm) (528-533). https://doi: 10.1109/SmartGridComm.2011.6102379.