| 优化循环神经网络在滑坡位移预测中的应用 |
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| 引用本文： | 李璐,瞿伟,张勤,李久元,王宇豪,刘祥斌.优化循环神经网络在滑坡位移预测中的应用[J].大地测量与地球动力学,2022,42(6):594-600. |
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| 作者姓名： | 李璐 瞿伟 张勤 李久元 王宇豪 刘祥斌 |
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| 作者单位： | 长安大学地质工程与测绘学院,西安市雁塔路 126 号,710054 |
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| 基金项目： | 中央高校基本科研业务费专项;陕西测绘地理信息局科技创新项目;陕西省留学人员科技择优资助项目;陕西省杰出青年科学基金;国家自然科学基金 |
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| 摘 要： | 以中国典型黄土滑坡域甘肃黑方台党川6#滑坡体为例，基于滑坡体北斗和位移计时序监测数据，首先利用深度学习框架Tensorflow分别构建3种循环神经网络滑坡位移预测模型：简单循环神经网络(simple recurrent neural network，SimpleRNN)、长短期记忆网络(long short-term memory，LSTM)和门控循环单元(gated recurrent unit，GRU)，并进一步针对循环神经网络在参数设置时多采用经验手动调参或采用网格搜索法，易造成人为主观影响较大和计算效率低下的突出问题，引入遗传算法（genetic algorithm，GA）优化循环神经网络参数的自动最佳化选取，分别构建3种基于遗传算法改进的循环神经网络滑坡位移高精度预测模型：GA-SimpleRNN、GA-LSTM、GA-GRU。研究结果表明，改进参数自动寻优后的3种循环神经网络预测模型具有更优的预测性能，特别是GA-GRU模型预测精度最高，更适用于滑坡体长时序位移的高精度预测。
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| 关 键 词： | 滑坡位移预测 简单循环神经网络 长短期记忆网络 门控循环单元 遗传算法 |
Application of Optimized Recurrent Neural Network in Prediction of Landslide Displacement |
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| LI Lu,QU Wei,ZHANG Qin,LI Jiuyuan,WANG Yuhao,LIU Xiangbin.Application of Optimized Recurrent Neural Network in Prediction of Landslide Displacement[J].Journal of Geodesy and Geodynamics,2022,42(6):594-600. |
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| Authors: | LI Lu QU Wei ZHANG Qin LI Jiuyuan WANG Yuhao LIU Xiangbin |
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| Abstract: | We select the Heifangtai Dangchuan #6 landslide body in Gansu province as the study region, which is a typical loess landslide area in Chinese mainland. Three recurrent neural network prediction models of landslide are established using the deep learning framework Tensorflow based on the monitoring data of Beidou and displacement meter, namely, the simple recurrent neural network(SimpleRNN), long short-term memory(LSTM), and gated recurrent unit(GRU). Further, in view of the prominent problems of large subjective impact and low computational efficiency caused by the fact that the parameters of the recurrent neural network are mostly adjusted manually by experience or the grid search method, we introduce a genetic algorithm(GA) to optimize the automatic optimization selection of the parameters of the recurrent neural network. Thereby, three recurrent neural network prediction models of landslide optimized by GA are established, namely, GA-SimpleRNN, GA-LSTM, and GA-GRU. The results show that the improved three recurrent neural network prediction models with automatic optimization of parameters have better prediction performance. The GA-GRU model has the highest prediction accuracy, which is more suitable for the high-precision prediction of the long-time displacement of landslides. |
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| Keywords: | landslide displacement prediction simple recurrent neural networks(SimpleRNN) long short-term memory(LSTM) gated recurrent unit(GRU) genetic algorithm(GA) |
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