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基于自适应加权平均多传感器的泥石流灾害预报模型 被引量:2

Based on adaptive weighted average multi-sensor debris flow prediction model
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摘要 针对泥石流监测中多传感器数据融合问题,提出了一种分批估计的自适应加权平均多传感器数据融合算法,该算法将同类无线传感器采集的数据融合。并建立粒子群算法优化极限学习机来预测泥石流的发生概率,仿真结果表明将融合后的数据输入改进极限学习机的泥石流灾害预报模型中,可以有效提高模型预报准确率以及模型训练时间。 Aiming at the problem of multi-sensor data fusion in debris flow monitoring,an adaptive weighted average multi-sensor data fusion algorithm is proposed for batch estimation,which will affect the rainfall,soil moisture content,pore water pressure,slope,infrasound,ditch bed ratio drop and relative height difference of debris flow.The particle swarm optimization limit learning machine is established to predict the probability of debris flow,and the activation function of the ELM is optimized.The input of the improved PSO-SELM is the selected disaster factor and the output is the probability of debris flow.The simulation results show that the fused data can be input into the debris flow disaster prediction model based on the improved limit learning machine,and the fused data can be compared with the data without data fusion.The prediction accuracy and training time of the model can be improved effectively.
作者 陈鹏年 李丽敏 温宗周 张阳阳 CHEN Pengnian;LI Limin;WEN Zongzhou;ZHANG Yangyang(Xi’an Engineering University School of Electronic Information,Xi’an 710600,China)
出处 《自动化与仪器仪表》 2021年第8期14-17,22,共5页 Automation & Instrumentation
基金 基于大数据分析和传感器信息融合的重大地质灾害实时在线监测预警系统:科技成果转移与推广计划-吸纳成果转化项目(供给方)(No.2020CGXNG-009)。
关键词 数据融合 粒子群 极限学习机 泥石流 data fusion particle swarm extreme learning machine debris flow
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