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Cascade-based multi-scale AI approach for modeling rainfall-runoff process

机译：基于级联的多尺度AI方法模拟降雨径流过程

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In this paper, runoff time series of the sub-basins in a cascade form were decomposed by Wavelet Transform (WT) to extract their dynamical and multi-scale features for modeling Multi-Station (MS) rainfall-runoff (R-R) process of the Little River Watershed (LRW) in USA. A Self-Organizing Map (SOM) clustering technique was also employed to find homogeneous extracted sub-series' clusters. As a complementary feature, extraction criterion of mutual information (MI) was utilized for proper cluster agent choice to impose to the artificial intelligence (AI) models (Feed Forward Neural Network, FFNN; Extreme Learning Machine, ELM; and Least Square Support Vector Machine, LSSVM) to predict the runoff of the LRW sub-basins. The performance of wavelet-based runoff prediction was compared to the Markovian-based MS model. The proposed method not only considers the prediction of the outlet runoff but also covers predictions of interior sub-basins behavior. The outcomes showed that the proposed AI-models combined with the SOM and MI tools enhanced the MS runoff prediction efficiency up to 23% in comparison with the Markovian-based models. Nevertheless, benefit of the seasonality of the process along with reduction of dimension of the inputs could help the AI-models to consume pure information of the recorded data.
机译：本文利用小波变换（WT）对子流域梯级流域的径流时间序列进行分解，提取其动态和多尺度特征，为多站（MS）降雨径流（RR）过程建模提供理论依据。小河流域（LRW）在美国。还采用了自组织图（SOM）聚类技术来查找均质提取的子系列聚类。作为补充功能，互信息（MI）的提取标准用于适当的集群代理选择，以施加到人工智能（AI）模型（前馈神经网络，FFNN；极限学习机，ELM；最小二乘支持向量机） ，LSSVM）来预测LRW子流域的径流。将基于小波的径流预测性能与基于马尔可夫的MS模型进行了比较。所提出的方法不仅考虑了出口径流的预测，而且还涵盖了内部子流域行为的预测。结果表明，与基于Markovian的模型相比，所提出的AI模型与SOM和MI工具相结合将MS径流预测效率提高了23％。然而，过程季节性的好处以及输入维数的减少可以帮助AI模型使用已记录数据的纯信息。

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