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Estimating reference evapotranspiration using a machine learning approach
Bhavya T R1, Ananta Vashisth2, P Krishnan1
1ICAR-Indian Agricultural Research Institute, Pusa, New Delhi 110 012, India.
Summary
Machine learning models accurately estimate evapotranspiration (ET0) using limited weather data. Random Forest, Support Vector Machine, and Artificial Neural Network models show potential for improved ET0 prediction in wheat-growing regions.
Area of Science:
- Agricultural Meteorology
- Artificial Intelligence in Hydrology
Background:
- Evapotranspiration (ET0) is crucial for crop water management.
- Accurate ET0 estimation relies on key weather parameters like temperature, solar radiation, humidity, and wind speed.
Purpose of the Study:
- To develop and compare machine learning models for improved ET0 estimation during wheat growth.
- To identify optimal weather input combinations for accurate ET0 prediction.
Main Methods:
- Collected daily weather data (1970-2018) for Amritsar, India.
- Developed Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models.
- Evaluated models using statistical criteria for calibration and validation.
Main Results:
- RF model demonstrated the best performance, followed by SVM and ANN.
- The model using Tmax, Tmin, RHM, RHE, and Rs achieved the highest rank.
- Specific input combinations (Rs, Tmax; Rs, Tmin; Tmax, Tmin) showed excellent performance with RF, SVM, and ANN, respectively.
Conclusions:
- Machine learning techniques can accurately estimate ET0 with fewer data points.
- Selected input combinations are suitable for ET0 estimation when data is limited.
- This approach enhances the practicality of ET0 estimation in data-scarce scenarios.
Keywords:
artificial neural networkevapotranspirationmachine learningrandom forestsupport vector machineMore Related Videos
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