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Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan
Milad Sharafi1, Saeed Samadianfard2, Javad Behmanesh3
1Department of Water Engineering, Urmia University, Urmia, Iran.
International Journal of Biometeorology
|December 7, 2023
Summary
The fruit fly algorithm (FOA) significantly enhances support vector regression (SVR) for predicting pan evaporation. The combined SVR-FOA model demonstrated superior accuracy compared to SVR and SVR-FFA, making it ideal for hydrological forecasting.
Area of Science:
- Hydrology and Meteorology
- Machine Learning Applications
Background:
- Pan evaporation is a critical factor in water resource management and agricultural planning.
- Accurate prediction of pan evaporation is essential for irrigation scheduling and drought assessment.
Purpose of the Study:
- To predict pan evaporation using Support Vector Regression (SVR), SVR coupled with the Fruit Fly Algorithm (SVR-FOA), and SVR coupled with the Firefly Algorithm (SVR-FFA).
- To evaluate the performance of these models for pan evaporation estimation at four stations in West Azerbaijan, Iran.
Main Methods:
- Meteorological data from 1990-2020 were collected.
- Pearson's correlation coefficient was used to identify significant input parameters (wind speed and sunshine hours).
- SVR, SVR-FOA, and SVR-FFA models were developed and compared for pan evaporation prediction.
Main Results:
- Wind speed and sunshine hours were identified as the most influential parameters for pan evaporation.
- SVR-FFA showed the best performance at Urmia station.
- SVR-FOA outperformed both SVR and SVR-FFA at other stations, exhibiting the lowest errors.
Conclusions:
- The Fruit Fly Algorithm (FOA) offers advantages like simplicity and efficiency, significantly improving SVR model capabilities.
- SVR-FOA is recommended as the most accurate and effective method for pan evaporation estimation in the studied region.
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