Application of Wavelet Neural Network in Estimation of Average Air-temperature
Standard weather station evaluates air-temperature and it is major descriptor for earth environment condition. Thus, estimation and estimation of average daily temperature is one of the main perquisites for agriculture programming and also water source management which is possible by empirical, quas...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Gorgan University of Agricultural Sciences and Natural Resources
2022-07-01
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| Series: | Environmental Resources Research |
| Subjects: | |
| Online Access: | https://ijerr.gau.ac.ir/article_6612_69a13d98246cbc78a96ec669d2db352f.pdf |
| Summary: | Standard weather station evaluates air-temperature and it is major descriptor for earth environment condition. Thus, estimation and estimation of average daily temperature is one of the main perquisites for agriculture programming and also water source management which is possible by empirical, quasi-empirical l and intelligent method. This study evaluates the application wavelet neural network (WNN) to estimation of average daily air-temperature in Sari weather station and also compares its efficiency with artificial neural network (ANN). It was used thermograph data of Sari weather station for modeling. Relative humidity, maximum temperature, minimum temperature, wind velocity and daily evaporation were considered as network input and air-temperature was considered as network output during 2010 to 2020 years. Criteria including correlation coefficient, root mean square error (RMSE), Nash-Sutcliffe (NS) coefficient were used to evaluate and comparison of models efficiency. Results showed that WNN model had better performance rather than ANN for modeling, so that WNN model showed the most coefficient of determination (0.999), RMSE (0.001) and NS (0.998) which was initiated in accuracy stage. In conclusion, results showed higher precision of WNN model in estimation air-temperature. |
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| ISSN: | 2783-4832 2783-4670 |