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...
Saved in:
Main Authors: | , , , , , , |
---|---|
Format: | EJournal Article |
Published: |
Institute of Advanced Engineering and Science,
2021-04-01.
|
Subjects: | |
Online Access: | Get fulltext |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | 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. |
---|---|
Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24183 |