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Comparison of Wavelet Based Hybrid Models for Daily Evapotranspiration Estimation using Meteorological Data

机译:基于小波的混合模型每日气象蒸发量估算的比较

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摘要

This paper investigates the comparative performance of wavelet based radial basis networks and multi linear regression in daily reference evapotranspiration estimation. The meteorological data (air temperature, solar radiation, wind speed, relative humidity) from two stations in the United States was evaluated for estimating models. The wavelet based radial basis network combines wavelet transformation and radial basis neural network, while the wavelet based regression model combines wavelet transformation and multi linear regression. The results show that the wavelet transformation has significantly positive effects on modeling performance. The wavelet based radial basis network provided the best performance evaluation criteria.
机译:本文研究了基于小波的径向基网络和多元线性回归在每日参考蒸散量估算中的比较性能。对来自美国两个站点的气象数据(气温,太阳辐射,风速,相对湿度)进行了评估,以评估模型。基于小波的径向基网络结合了小波变换和径向基神经网络,而基于小波的回归模型则结合了小波变换和多元线性回归。结果表明,小波变换对建模性能具有明显的积极影响。基于小波的径向基网络提供了最佳的性能评估标准。

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