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首页> 外文期刊>E3S Web of Conferences >Improving the performance of streamflow forecasting model using data-preprocessing technique in Dungun River Basin
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Improving the performance of streamflow forecasting model using data-preprocessing technique in Dungun River Basin

机译：利用数据预处理技术改善敦煌河流域流量预报模型的性能

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An accurate streamflow forecasting model is important for the development of flood mitigation plan as to ensure sustainable development for a river basin. This study adopted Variational Mode Decomposition (VMD) data-preprocessing technique to process and denoise the rainfall data before putting into the Support Vector Machine (SVM) streamflow forecasting model in order to improve the performance of the selected model. Rainfall data and river water level data for the period of 1996-2016 were used for this purpose. Homogeneity tests (Standard Normal Homogeneity Test, the Buishand Range Test, the Pettitt Test and the Von Neumann Ratio Test) and normality tests (Shapiro-Wilk Test, Anderson-Darling Test, Lilliefors Test and Jarque-Bera Test) had been carried out on the rainfall series. Homogenous and non-normally distributed data were found in all the stations, respectively. From the recorded rainfall data, it was observed that Dungun River Basin possessed higher monthly rainfall from November to February, which was during the Northeast Monsoon. Thus, the monthly and seasonal rainfall series of this monsoon would be the main focus for this research as floods usually happen during the Northeast Monsoon period. The predicted water levels from SVM model were assessed with the observed water level using non-parametric statistical tests (Biased Method, Kendall’s Tau B Test and Spearman’s Rho Test).
机译：准确的流量预测模型对于防洪计划的制定至关重要，以确保流域的可持续发展。本研究采用变异模式分解（VMD）数据预处理技术对降雨数据进行处理和去噪，然后再将其应用到支持向量机（SVM）流量预测模型中，以提高所选模型的性能。为此，使用了1996-2016年期间的降雨数据和河水水位数据。已经进行了均质性测试（标准均质性测试，Buishand范围测试，Pettitt测试和冯·诺伊曼比率测试）和正态性测试（Shapiro-Wilk测试，Anderson-Darling测试，Lilliefors测试和Jarque-Bera测试）。降雨系列。在所有站中分别发现了同质和非正态分布的数据。从记录的降雨数据中可以看出，在东北季风期间，从11月到2月，Dunkun流域的月降雨量更高。因此，由于洪水通常发生在东北季风时期，因此该季风的月度和季节性降雨序列将成为本研究的重点。使用非参数统计检验（偏见方法，Kendall的Tau B检验和Spearman的Rho检验），利用观察到的水位评估了SVM模型中的预测水位。

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