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Rana Muhammad Adnan
Hohai University Nanjing

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Air temperature prediction using different machine learning models Rana Muhammad Adnan; Zhongmin Liang; Alban Kuriqi; Ozgur Kisi; Anurag Malik; Binquan Li; Fatemehsadat Mortazavizadeh
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp534-541

Abstract

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.
Co-Authors Alban Kuriqi Anurag Malik Binquan Li Fatemehsadat Mortazavizadeh Ozgur Kisi Zhongmin Liang