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首页> 外文期刊>Journal of Hydroinformatics >Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling
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Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling

机译：情感神经网络（EANN）与小波变换的结合，用于降雨径流建模

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摘要

The current research introduces a combined wavelet-emotional artificial neural network (WEANN) approach for one-time-ahead rainfall-runoff modeling of two watersheds with different geomorphological and land cover conditions at daily and monthly time scales, to utilize within a unique framework the ability of both wavelet transform (to mitigate the effects of non-stationary) and emotional artificial neural network (EANN, to identify and individualize wet and dry conditions by hormonal components of the artificial emotional system). To assess the efficiency of the proposed hybrid model, the model efficiency was also compared with so-called EANN models (as a new generation of ANN-based models) and wavelet-ANN (WANN) models (as a multi-resolution forecasting tool). The obtained results indicated that for daily scale modeling, WEANN outperforms the other models (EANN and WANN). Also, the obtained results for monthly modeling showed that WEANN could outperform the WANN and EANN models up to 17% and 35% in terms of validation and training efficiency criteria, respectively. Also, the obtained results highlighted the capability of the proposed WEANN approach to better learning of extraordinary and extreme conditions of the process in the training phase.
机译：当前的研究引入了一种结合小波情感人工神经网络（WEANN）的方法，可以在每日和每月的时间尺度上对两个具有不同地貌和土地覆盖条件的流域进行一次一次性降雨-径流建模，以在一个独特的框架内利用小波变换（缓解非平稳效应）和情感人工神经网络（EANN，通过人工情感系统的荷尔蒙成分识别和个性化干燥和干燥条件）的能力。为了评估提出的混合模型的效率，还将模型效率与所谓的EANN模型（作为基于ANN的新一代模型）和小波ANN（WANN）模型（作为多分辨率预测工具）进行比较。获得的结果表明，在日常规模建模中，WEANN优于其他模型（EANN和WANN）。此外，所获得的每月建模结果表明，就验证和培训效率标准而言，WEANN的性能可分别比WANN和EANN模型高17％和35％。而且，获得的结果突出了所提出的WEANN方法在培训阶段更好地学习过程的异常和极端条件的能力。

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