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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

Air Temperature Prediction, Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Climate Change, Weather Forecast

Author ORCID ID

https://orcid.org/0000-0001-6027-0318

Co-Authors ORCID ID

https://orcid.org/0000-0001-8536-3641

https://orcid.org/0000-0003-3088-2377

Date

12-4-2021

Document Type

Original article

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