[1]徐根祺,李丽敏*,温宗周,等.基于宽度学习模型的泥石流灾害预报[J].山地学报,2019,(6):868-878.[doi:10.16089/j.cnki.1008-2786.000477]
 XU Genqi,LI Limin*,WEN Zongzhou,et al.Debris Flow Disaster Prediction Based on Broad Learning Model[J].Mountain Research,2019,(6):868-878.[doi:10.16089/j.cnki.1008-2786.000477]
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基于宽度学习模型的泥石流灾害预报()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2019年第6期
页码:
868-878
栏目:
山地灾害
出版日期:
2019-11-30

文章信息/Info

Title:
Debris Flow Disaster Prediction Based on Broad Learning Model
文章编号:
1008-2786-(2019)6-868-11
作者:
徐根祺李丽敏* 温宗周刘德阳程少康
西安工程大学 电子信息学院,西安 710048
Author(s):
XU Genqi LI Limin* WEN Zongzhou LIU Deyang CHENG Shaokang
College of Electronic Information, Xi'an Polytechnic University, Xi'an 710048, China
关键词:
宽度学习算法 泥石流 预报模型 奇异值分解
Keywords:
broad learning algorithm debris flow forecasting model singular value decomposition
分类号:
P642~A
DOI:
10.16089/j.cnki.1008-2786.000477
摘要:
在泥石流灾害预报模型研究中,科学确定泥石流灾害影响因子及保证模型较高的预报准确率和快速的训练速度是关键问题,也是泥石流灾害预报预警和防灾减灾的重要基础。本研究针对目前泥石流预报模型输入数据维度较大和训练时间较长的问题,采用快速多个主成分并行提取算法(Fast multiple principle components extraction algorithm,FMPCE),选取出6个泥石流灾害影响因子,包括降雨量、山坡坡度、沟床比降、相对高差、土壤含水率和孔隙水压力。基于宽度学习(Broad learning,BL)算法,以泥石流影响因子为输入,泥石流发生概率为输出,构建了泥石流预报模型,并用矩阵随机近似奇异值分解(矩阵随机近似SVD)对模型进行了优化,将优化后宽度学习模型的预报结果与梯度下降法优化的BP神经网络预报模型(GD-BP)、基于支持向量机的预报模型(SVM)、宽度学习预报模型(BL)的结果进行对比,同时,通过输入数据集的扩展,从训练时间上对不同模型进行比较。结果表明,优化宽度学习泥石流灾害预报模型的预报准确率为93.52%,较GD-BP模型、SVM模型和BL模型的预报准确率分别高出1.60%、1.15%和0.03%; 优化宽度学习泥石流灾害预报模型的训练时间为0.9039s,较GD-BP模型、SVM模型和BL模型的训练时间分别节省了25.3867 s、17.2620 s和0.8974 s。本研究说明宽度学习算法可以用于对泥石流灾害的发生概率进行预报,同时也可为泥石流预报的实际应用提供新的思路。
Abstract:
In the study of debris flow disaster forecasting model, it is a key problem to scientifically determine the influencing factors of debris flow disasters and ensure the higher forecast accuracy and fast training speed of the model, which is also an important foundation for debris flow disaster forecasting and early warning and disaster prevention and mitigation as well.Aiming at the problems of large dimension of input data and long training time of current debris flow prediction models, this paper used a fast-multiple principle components extraction algorithm(FMPCE)to select 6 influence factors of debris flow disaster, including rainfall volume, hillside slope, ditch bed ratio, relative height difference, soil moisture content and pore water pressure. Based on the broad learning(BL)algorithm, the influence factors of debris flow were taken as inputs and debris flow occurrence probability as output to construct a debris flow forecasting model, and the model was optimized by using matrix random approximate singular value decomposition(matrix random approximation SVD). The prediction results of the optimized broad learning model were compared with the results of the BP neural network prediction model(GD-BP)optimized by the gradient descent method, the support vector machine-based prediction model(SVM), and the broad learning prediction model(BL). At the same time, through the expansion of the input data set, different models were compared from the training time. The results showed that the prediction accuracy of the optimized broad learning debris flow disaster prediction model was 93.52%, which was 1.60%, 1.15%, and 0.03% higher than the prediction accuracy of the GD-BP model, the SVM model, and the BL model; The training time of the optimized broad learning debris flow disaster prediction model was 0.9039 s, which saved 25.3867 s, 17.2620 s, and 0.8974 s compared with the training time of the GD-BP model, SVM model, and BL model, respectively, which showed that the broad learning algorithm can be used to predict the occurrence probability of debris flow disasters, and provided a new idea for the practical application of debris flow prediction as well.

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备注/Memo

备注/Memo:
收稿日期(Received date):2019-06-13; 改回日期(Accepted date):2020-01-02
基金项目(Foundation item):陕西省自然科学基础研究计划资助项目(2019JQ-206)。[Natural Science Basic Research Plan in Shaanxi Province of China(2019JQ-206)]
作者简介(Biography):徐根祺(1984-),男,陕西西安人,硕士研究生,工程师,主要研究方向:人工智能算法。 [XU Genqi(1984-), male, born in Xi'an, Shanxi province, M.Sc. candidate, engineer, research on artificial intelligence algorithms AI] E-mail: 2017041028@stu.xpu.edu.cn
更新日期/Last Update: 2019-11-30