| 集成极限学习机的中小河流洪水预报方法研究 |
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| 引用本文： | 孔俊,李士进,朱跃龙.集成极限学习机的中小河流洪水预报方法研究[J].水文,2018,38(1):67-72. |
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| 作者姓名： | 孔俊 李士进 朱跃龙 |
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| 作者单位： | 河海大学计算机与信息学院; |
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| 基金项目： | 公益性行业科研专项(201501022)；江苏省重点研发计划项目(BE2015707)； |
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| 摘 要： | 为利用水文现象相似性和极限学习机(ELM)集成学习提高洪水预报精度,提出了一种基于相似度匹配的集成ELM洪水预报方法(SM-ELM)。方法首先从多个ELM模型中,为每一个训练样本找到最优的ELM模型,然后从训练集中,为测试样本匹配出最相似的前k个训练样本,最后利用这k个训练样本分别对应的最优ELM模型,对测试样本采用加权平均法进行集成预报。为证明提出方法的可行性和有效性,以昌化流域的历史洪水为例进行了验证。结果表明,相对于单个ELM,集成ELM模型能有效地提高预测精度。从均方根误差上看,集成ELM模型性能比单个ELM模型提升了10%~15%。在三种集成方法中,SM-ELM能够以较少的模型数量获得较高且稳定的预报精度。
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| 关 键 词： | 相似性 极限学习机 集成学习 洪水预报 |
| 收稿时间： | 2017/4/5 0:00:00 |
Flood Forecasting for Small- and Medium-sized Rivers by Ensemble Extreme Learning Machine |
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| KONG Jun,LI Shijin,ZHU Yuelong.Flood Forecasting for Small- and Medium-sized Rivers by Ensemble Extreme Learning Machine[J].Hydrology,2018,38(1):67-72. |
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| Authors: | KONG Jun LI Shijin ZHU Yuelong |
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| Abstract: | In order to take the advantages of hydrological similarity and ensemble extreme learning machine (ELM) to improveflood forecasting accuracy, we proposed a flood forecasting method for small- and medium-sized rivers, which is ensemble extremelearning machine based on similarity matching (SM-ELM). Firstly, we found the optimal ELM for each training sample from manyELMs. Then, we found k training samples that are most similar with the testing sample from the training set. At last, we used theoptimal ELMs of the k training samples to predict testing sample and combine the model outputs through weighted average strategy.We took the historical floods in the Chuanghua River Basin as study case to prove the validity of the proposed method. Theresults indicate that the ensemble ELM significantly improve the flood forecasting accuracy as compared with the single ELM. Theaccuracy of ensemble ELM is about 10%-15% higher than that of the single ELM in terms of root mean square error. Among thethree ensemble methods, SM-ELM can achieve higher and more stable forecasting accuracy with less number of ELMs. |
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| Keywords: | similarity extreme learning machine ensemble learning flood forecasting |
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