Air temperature prediction using different machine learning models
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 d...
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| Main Authors: | , , , , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-04-01.
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| Online Access: | Get fulltext |
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| LEADER | 02452 am a22003613u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24183_14848 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Adnan, Rana Muhammad |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Liang, Zhongmin |e author |
| 700 | 1 | 0 | |a Kuriqi, Alban |e author |
| 700 | 1 | 0 | |a Kisi, Ozgur |e author |
| 700 | 1 | 0 | |a Malik, Anurag |e author |
| 700 | 1 | 0 | |a Li, Binquan |e author |
| 700 | 1 | 0 | |a Mortazavizadeh, Fatemehsadat |e author |
| 245 | 0 | 0 | |a Air temperature prediction using different machine learning models |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-04-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24183 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Astore river; CART; Gilgit river; GMDH-NN; LSSVM | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 1: April 2021; 534-541 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24183/14848 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24183/14848 |z Get fulltext |