粒子群优化BP神经网络的滑坡敏感性评价
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摘要
滑坡敏感性评价是地质灾害预测预报的关键环节。针对BP神经网络易陷入局部最小值、收敛速度慢等问题,该文以三峡库区秭归县境内为研究区,采用粒子群优化(PSO)算法对BP神经网络的初始权值和阈值进行优化,构建PSO-BP神经网络滑坡敏感性预测模型,实现研究区滑坡敏感性评价。采用受试者工作特征曲线分析模型预测精度,得到PSO-BP神经网络预测精度为0.931,预测结果与实际滑坡总体空间分布具有良好的一致性,且预测能力优于BP神经网络。实验结果表明,PSO-BP神经网络耦合模型在实现滑坡敏感性评价上具有理想的预测精度和良好的适用性。
Landslide susceptibility assessment is of great importance in predicting and forecasting geological hazards.This paper presented the landslide susceptibility assessment on the Zigui County of the Three Gorges using aparticle swarm-optimized BP neural network.The particle swarm optimization algorithm(PSO)was used to optimize the initial weight and threshold parameters of BP neural network.The analytical results were validated by comparing them with known landslides using a receiver operator characteristic curve,and the particle swarm-optimized BP neural network model has a higher accuracy than BP neural network model,with an area ratio of 0.931.The validation results displayed sufficient agreement between the predicted results and the spatial distribution of existing landslides,which showed that the proposed method has higher prediction accuracy and good suitability in landslide susceptibility assessment.
Landslide susceptibility assessment is of great importance in predicting and forecasting geological hazards.This paper presented the landslide susceptibility assessment on the Zigui County of the Three Gorges using aparticle swarm-optimized BP neural network.The particle swarm optimization algorithm(PSO)was used to optimize the initial weight and threshold parameters of BP neural network.The analytical results were validated by comparing them with known landslides using a receiver operator characteristic curve,and the particle swarm-optimized BP neural network model has a higher accuracy than BP neural network model,with an area ratio of 0.931.The validation results displayed sufficient agreement between the predicted results and the spatial distribution of existing landslides,which showed that the proposed method has higher prediction accuracy and good suitability in landslide susceptibility assessment.
引文
[1]殷坤龙.滑坡灾害预测预报[M].武汉:中国地质大学出版社,2004:17.(YIN Kunlong.Landslide hazard prediction and evaluation[M].Wuhan:China University of Geosciences Press,2004:17.)
[2]PRADHAN B,LEE S,BUCHROITHNER M F.A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses[J].Computers,Environment and Urban Systems,2010,34(3):216-235.
[3]WANG H B,XU W Y,XU R C.Slope stability evaluation using back propagation neural networks[J].Engineering Geology,2005,80(3):302-315.
[4]柯福阳,李亚云.基于BP神经网络的滑坡地质灾害预测方法[J].工程勘察,2014,42(8):55-60.(KE Fuyang,LI Yayun.The forecasting method of landslides based on improved BP neural network[J].Geotechnical Investigation&Surverying,2014,42(8):55-60.)
[5]马锐.人工神经网络原理[M].北京:机械工业出版社,2010:48-52.(MA Rui.Principle of artificial neural network[M].Beijing:China Machine Press,2010:48-52.)
[6]KENNEDY J,EBERHART R C.Particle swarm optimization[C]//IEEE International Conference on Neural Networks.Washington,DC,USA:IEEE,1995:1942-1948.
[7]NAVALERTPORN T,AFZULPURKAR N V.Optimization of tile manufacturing process using particle swarm optimization[J].Swarm and Evolutionary Computation,2011,1(2):97-109.
[8]倪庆剑,邢汉承,张志政,等.粒子群优化算法研究进展[J].模式识别与人工智能,2007,20(3):349-357.(NI Qingjian,XING Hancheng,ZHANG Zhizheng,et al.Survey of particle swarm optimization algorithm[J].Pattem Recognition and Aitificial Intelligence,2007,20(3):349-357.)
[9]ALADAG C H,YOLCU U,EGRIOGLU E.A new multiplicative seasonal neural network model based on particle swarm optimization[J].Neural Processing Letters,2013,37(3):251-262.
[10]彭令.三峡库区滑坡灾害风险评估研究[D].武汉:中国地质大学,2013:42-67.(PENG Ling.Landslide risk assessment in the Three Gorges Reservoir[D].Wuhan:China University of Geosciences,2013:42-67.)
[11]武雪玲,任福,牛瑞卿.多源数据支持下的三峡库区滑坡灾害空间智能预测[J].武汉大学学报(信息科学版),2013,38(8):963-968.(WU Xueling,REN Fu,NIU Ruiqing.Spatial intelligent prediction of landslide hazard based on multi-source data in Three Gorges Reservoir area[J].Geomatics and Information Science of Wuhan University,2013,38(8):963-968.)
[12]葛哲学,孙志强.神经网络理论与MATLAB R2007实现[M].北京:电子工业出版社,2007:111.(GE Zhexue,SUN Zhiqiang.Neural network theory and MATLAB R2007application[M].Beijing:Publishing House of Electronics Industry,2007:111.)
[2]PRADHAN B,LEE S,BUCHROITHNER M F.A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses[J].Computers,Environment and Urban Systems,2010,34(3):216-235.
[3]WANG H B,XU W Y,XU R C.Slope stability evaluation using back propagation neural networks[J].Engineering Geology,2005,80(3):302-315.
[4]柯福阳,李亚云.基于BP神经网络的滑坡地质灾害预测方法[J].工程勘察,2014,42(8):55-60.(KE Fuyang,LI Yayun.The forecasting method of landslides based on improved BP neural network[J].Geotechnical Investigation&Surverying,2014,42(8):55-60.)
[5]马锐.人工神经网络原理[M].北京:机械工业出版社,2010:48-52.(MA Rui.Principle of artificial neural network[M].Beijing:China Machine Press,2010:48-52.)
[6]KENNEDY J,EBERHART R C.Particle swarm optimization[C]//IEEE International Conference on Neural Networks.Washington,DC,USA:IEEE,1995:1942-1948.
[7]NAVALERTPORN T,AFZULPURKAR N V.Optimization of tile manufacturing process using particle swarm optimization[J].Swarm and Evolutionary Computation,2011,1(2):97-109.
[8]倪庆剑,邢汉承,张志政,等.粒子群优化算法研究进展[J].模式识别与人工智能,2007,20(3):349-357.(NI Qingjian,XING Hancheng,ZHANG Zhizheng,et al.Survey of particle swarm optimization algorithm[J].Pattem Recognition and Aitificial Intelligence,2007,20(3):349-357.)
[9]ALADAG C H,YOLCU U,EGRIOGLU E.A new multiplicative seasonal neural network model based on particle swarm optimization[J].Neural Processing Letters,2013,37(3):251-262.
[10]彭令.三峡库区滑坡灾害风险评估研究[D].武汉:中国地质大学,2013:42-67.(PENG Ling.Landslide risk assessment in the Three Gorges Reservoir[D].Wuhan:China University of Geosciences,2013:42-67.)
[11]武雪玲,任福,牛瑞卿.多源数据支持下的三峡库区滑坡灾害空间智能预测[J].武汉大学学报(信息科学版),2013,38(8):963-968.(WU Xueling,REN Fu,NIU Ruiqing.Spatial intelligent prediction of landslide hazard based on multi-source data in Three Gorges Reservoir area[J].Geomatics and Information Science of Wuhan University,2013,38(8):963-968.)
[12]葛哲学,孙志强.神经网络理论与MATLAB R2007实现[M].北京:电子工业出版社,2007:111.(GE Zhexue,SUN Zhiqiang.Neural network theory and MATLAB R2007application[M].Beijing:Publishing House of Electronics Industry,2007:111.)