Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques
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- Multivariate Time Series Evapotranspiration Forecasting using Machine Learning Techniques
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Time Series Analysis of Reference Crop Evapotranspiration Using Machine Learning Techniques For Ganjam District, Odisha, India
ICCDA '18: Proceedings of the 2nd International Conference on Compute and Data AnalysisEvapotranspiration (ET0) influences water resources and it is considered as a vital process in aridic hydrologic frameworks. It is one of the most important measure in finding the drought condition. Therefore, time series forecasting of ...
Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency
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Highlights- Ten statistical, machine learning, and deep learning models forecasted ETo.
- Data from 107 California weather stations were categorized based on their length.
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AbstractReference evapotranspiration (ETo) is an essential variable in agricultural water resources management and irrigation scheduling. An accurate and reliable forecast of ETo facilitates effective decision-making in agriculture. Although numerous ...
Software for estimating reference evapotranspiration using limited weather data
The FAO-56 Penman-Monteith combination equation (FAO-56 PM) has been recommended as the standard equation for estimating reference evapotranspiration (ET"0). The FAO-56 PM equation requires the numerous weather data that are not available in the most of ...
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- Conference Chairs:
- Jiman Hong,
- Maart Lanperne,
- Program Chairs:
- Juw Won Park,
- Tomas Cerny,
- Publication Chair:
- Hossain Shahriar
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Association for Computing Machinery
New York, NY, United States
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