Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches
نویسندگان
چکیده
منابع مشابه
Climate change impacts on spatial-temporal variations of reference evapotranspiration in Iran
Global warming due to greenhouse effect is expected to cause major changes in climate of some regions. The change of climate is likely to have a profound effect on hydrological cycle. Evapotranspiration (ET, as the major component of hydrological cycle, will affect crop water requirement and future planning and management of water resources. This study was conducted to investigate climate chang...
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Reference Evapotranspiration (ET0) is a basic parameter for determination of irrigation program. It is one of the most important factors in water resources management and considers as a requirement for every irrigation and drainage plans. Because of the spatial variations of ET0, it is necessary to use from intrapolation methods in order to estimation of this parameter in regional studie. Using...
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Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, artificial neural networks, neural network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficienc...
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The measurement of evapotranspiration is the most important factor in irrigation scheduling. Evapotranspiration means loss of water from the surface of plant and soil. Evaporation parameters are being used in studying water balances, water resource management, and irrigation system design and for estimating plant growth and height as well. Evapotranspiration is measured by different methods by ...
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This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Ra...
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ژورنال
عنوان ژورنال: Hydrology
سال: 2021
ISSN: 2306-5338
DOI: 10.3390/hydrology8010025