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首页> 外文期刊>Journal of Hydroinformatics >Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data
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Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data

机译：多变量自适应回归样条与耦合小波变换人工神经网络用于有限数据喜马拉雅流域径流预报的比较

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摘要

Himalayan watersheds are characterized by mountainous topography and a lack of available data. Due to the complexity of rainfall-runoff relationships in mountainous watersheds and the lack of hydrological data in many of these watersheds, process-based models have limited applicability for runoff forecasting in these areas, in light of this, accurate forecasting methods that do not necessitate extensive data sets are required for runoff forecasting in mountainous watersheds. In this study, multivariate adaptive regression spline (MARS), wavelet transform artificial neural network (WA-ANN), and regular artificial neural network (ANN) models were developed and compared for runoff forecasting applications in the mountainous watershed of Sainji in the Himalayas, an area with limited data for runoff forecasting. To develop and test the models, three micro-watersheds were gauged in the Sainji watershed in Uttaranchal State in India and data were recorded from July 1 2001 to June 30 2003. It was determined that the best WA-ANN and MARS models were comparable in terms of forecasting accuracy, with both providing very accurate runoff forecasts compared to the best ANN model. The results indicate that the WA-ANN and MARS methods are promising new methods of short-term runoff forecasting in mountainous watersheds with limited data, and warrant additional study.
机译：喜马拉雅流域的特点是山区地形和缺乏可用数据。由于山区流域降雨-径流关系的复杂性以及这些流域中许多流域的水文数据不足，基于过程的模型在这些地区进行径流预报的适用性有限，因此，准确的预报方法是不必要的山区流域的径流预报需要大量数据集。在这项研究中，开发了多元自适应回归样条（MARS），小波变换人工神经网络（WA-ANN）和常规人工神经网络（ANN）模型，并比较了喜马拉雅山塞因山区流域的径流预报应用，用于径流预报的数据有限的区域。为了开发和测试模型，在印度Uttaranchal州的Sainji流域对三个微流域进行了测量，并记录了2001年7月1日至2003年6月30日的数据。确定了最佳WA-ANN和MARS模型具有可比性。与最佳ANN模型相比，两者都提供了非常准确的径流预测。结果表明，WA-ANN和MARS方法是有前途的山区流域短期径流预报的新方法，数据有限，值得进一步研究。

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