Document Type : Original Article

Authors

Kurdistan Technical Institute, Sulaymaniyah

10.24271/psr.21

Abstract

Climate change has a historical impact at universal and local levels over the past era. Climate change is one of the greatest challenge issues in the globe for meteorological research. Air temperature estimation, in particular, has been measured as a significant feature in weather impression studies on industrial sectors, environmental, ecological, and agricultural. Accurately predicting air temperature guides to measure lifestyle, perform a key character for the government, industries, and public in development activities. In this paper, we investigate the use of various data mining approaches such as Support Vector Machine (SVM), Decision tree (DT), and Naïve Bayes for air temperature prediction within Sulaymaniyah City in Kurdistan, IRAQ. The metrological data is collected from the local Weather Forecast Department in the city within the range 2013 to 2018 inclusive. A dataset for the metrological data was developed and used to train the data mining algorithms. The proposed data mining algorithms were tested on the dataset to predict the air temperature and the performance of these algorithms were compared using standard performance metrics. Support vector machine has accomplished promising performance among using algorithms

Keywords

  1. References

    1. G. V. Krishna, “A Review of Weather Forecasting Models-Based on Data Mining and Artificial Neural Networks,” Comput. Scince Electron. J., vol. 6, pp. 214–222, 2015, doi: 10.090592/IJCSC.2015.617.
    2. A. You, M. A. Y. Be, and I. In, “Bayesian network model for temperature forecasting in Dubai Dubai,” vol. 100006, no. October, 2018, doi: 10.1063/1.5064935.
    3. J. Cifuentes, G. Marulanda, A. Bello, and J. Reneses, “Air temperature forecasting using machine learning techniques: A review,” Energies, vol. 13, no. 6, pp. 1–28, 2020, doi: 10.3390/en13164215.
    4. D. Chauhan and J. Thakur, “Data Mining Techniques for Weather Prediction: A Review,” Int. J. Recent Innov. Trends Comput. Commun., vol. 2, no. 8, pp. 2184–2189, 2014.
    5. Y. Mohammed, “COMPARABLE INVESTIGATION FOR RAINFALL FORECASTING USING DIFFERENT DATA MINING APPROACHES IN SULAYMANIYAH CITY IN IRAQ,” vol. 4, no. 1, pp. 11–18, 2020, doi: 10.18488/journal.72.2020.41.11.18.
    6. F. Olaiya and A. B. Adeyemo, “Application of Data Mining Techniques in Weather Prediction and Climate Change Studies,” Int. J. Inf. Eng. Electron. Bus., vol. 4, no. 1, pp. 51–59, 2012, doi: 10.5815/ijieeb.2012.01.07.
    7. C. Sahu, “A N I NTELLIGENT A PPLICATION O F F UZZY I D 3 T O,” vol. 2, no. 1, pp. 17–22, 2013.
    8. S. Badhiye, “Temperature and Humidity Data Analysis for Future Value Prediction using Clustering Technique : An Approach,” no. February 2012, 2015.
    9. A. Sharaff and S. R. Roy, “Comparative Analysis of Temperature Prediction Using Regression Methods and Back Propagation Neural Network,” 2018 2nd Int. Conf. Trends Electron. Informatics, no. Icoei, pp. 739–742, 2018.
    10. L. Houthuys, Z. Karevan, and J. A. K. Suykens, “Multi-View LS-SVM Regression for Black-Box Temperature Prediction in Weather Forecasting,” pp. 1102–1108, 2017.
    11. M. Yadav, S. Jain, and K. R. Seeja, Prediction of air quality using time series data mining, vol. 56. Springer Singapore, 2019.
    12. M. Yesilbudak, S. Sagiroglu, and I. Colak, “A new approach to very short term wind speed prediction using k -nearest neighbor classification,” ENERGY Convers. Manag., vol. 69, pp. 77–86, 2013, doi: 10.1016/j.enconman.2013.01.033.
    13. S. Governate, “Sulaymaniyah Governate,” 2018. [Online]. Available: http://slemani.gov.krd/so/pageDetail.php?secID=3#.
    14. P. Unit, “Sulaymaniyah Governorate Profile,” no. December 2015.
    15. Z. U. Khan and M. Hayat, “Hourly Based Climate Prediction Using Data Mining Techniquesby Comprising Entity Demean Algorithm,” vol. 21, no. 8, pp. 1295–1300, 2014, doi: 10.5829/idosi.mejsr.2014.21.08.21413.
    16. M. Muqeem and N. Javed, “A Critical Review of Data Mining Techniques in Weather Forecasting,” vol. 5, no. 4, pp. 1091–1094, 2016, doi: 10.17148/IJARCCE.2016.54266.
    17. S. D. Jadhav and H. P. Channe, “Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques,” Int. J. Sci. Res., vol. 5, no. 1, pp. 1842–1845, 2016, doi: 10.21275/v5i1.nov153131.
    18. Y. M. M. Salih, T. Cevik, and A. Kattan, “Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks,” Int. J. Nat. Sci. Res., vol. 4, no. 3, pp. 56–67, 2016, doi: 10.18488/journal.63/2016.4.3/63.3.56.67.
    19. A. Kulkarni, D. Chong, and F. A. Batarseh, Foundations of data imbalance and solutions for a data democracy. Elsevier Inc., 2020.
    20. A. Hanley, J. Mcneil, and D. Ph, “under a Receiver Characteristic,” pp. 29–36.