小波变换混合模型在流域径流预报中的精度研究

IF 1.2 Q4 WATER RESOURCES
Mohammad Javad Saravani, Sahar Kashef, Mahdi Farmahini, Mahdi Kashefi, Mahdi Zohreh
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引用次数: 1

摘要

径流降水是水文循环中最重要、最复杂的现象之一。为了发展和改进预测模型,对其建模提出了不同的观点。在人工智能技术的帮助下,水文过程可以自信地建模。采用人工神经网络(ann)、M5模型树及其与小波变换的混合方法对雷兰柴流域径流进行了模拟。从2000年到2021年,训练状态中使用的70%的数据和测试状态中使用的30%的数据都是在这个分水岭收集的。除日量表和月量表外,还对每个量表内的模拟结果和观测结果进行了比较。首先,利用小波变换将降雨和径流时间序列划分为多个子序列,以对抗不稳定性。然后将生成的小标题用作ANN和M5模型树的输入。结果表明,小波混合模型将人工神经网络模型的日准确率提高了4%,月准确率提高了26%。它还将M5模型树的日和月准确率分别提高了4%和41%。随着预测水平的延长,小波- m5模型的精度下降程度与小波-人工神经网络(WANN)模型不同。因此,雷兰柴流域具有相对稳定的行为模式。最后,混合模型与小波变换相结合,提高了预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Accuracy of Hybrid Models with Wavelet Transform in the Forecast of Watershed Runoff
In the hydrological cycle, runoff precipitation is one of the most significant and complex phenomena. In order to develop and improve predictive models, different perspectives have been presented in its modeling. Hydrological processes can be confidently modeled with the help of artificial intelligence techniques. In this study, the runoff of the Leilanchai watershed was simulated using artificial neural networks (ANNs) and M5 model tree methods and their hybrid with wavelet transform. Seventy percent of the data used in the train state and thirty percent in the test state were collected in this watershed from 2000 to 2021. In addition to daily and monthly scales, simulated and observed results were compared within each scale. Initially, the rainfall and runoff time series were divided into multiple sub-series using the wavelet transform to combat instability. The resultant subheadings were then utilized as input for an ANN and M5 model tree. The results demonstrated that hybrid models with wavelet improved the ANN model's daily accuracy by 4% and its monthly accuracy by 26%. It also improved the M5 model tree's daily and monthly accuracy by 4% and 41%. The wavelet-M5 model's accuracy does not diminish to the same degree as the wavelet-ANN (WANN) model as the forecast horizon lengthens. Consequently, the Leilanchai watershed has a relatively stable behavior pattern. Finally, hybrid models, in conjunction with the wavelet transform, improve forecast accuracy.
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CiteScore
1.30
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0.00%
发文量
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