基于麻雀搜索算法优化Elman残差自校正地面沉降预测模型

Elman Residual Self-tuning Land Subsidence Prediction Model Based on Sparrow Search Algorithm Optimization

地面沉降是一种常见的地质灾害,严重阻碍当地居民的生产生活,如何对地面沉降进行准确预测已经成为相关专家学者讨论的热点话题,但常规数学模型难以对地面沉降量做出准确预测。提出了麻雀搜索算法(sparrow search algorithm,SSA)优化Elman的地面沉降量预测方法,同时根据组合模型原理提出了SSA-Elman残差自校正(SSA-Elman residual self-correction,SSA-Elman-RSC)模型的策略,通过残差校正的方式降低神经网络预测误差,成功地将地面沉降量预测模型应用于山西省大同市潇河产业园,将预测结果与未进行残差修正的模型预测结果进行比较分析。结果表明,对于均方根误差(root mean squared error,RMSE)、平均绝对误差(mean absolute error,MAE)、均方误差(mean square error,MSE)3个指标,SSA-Elman-RSC拥有更高的精度。该模型的提出为山西地区地面沉降量预测提供了一种新方法,并且组合模型的建立提供了一种新思路。

Land subsidence is a common geological disaster,which seriously hinders the production and life of local residents.How to accurately predict land subsidence has become a hot topic discussed by relevant experts and scholars.But the conventional mathematical model is difficult to predict the land subsidence accurately.The sparrow search algorithm(SSA)was proposed to optimize the Elman land subsidence prediction method.At the same time,according to the principle of combination model,the strategy of SSAElman residual self-correction(SSA-Elman-RSC)model was proposed.The prediction error of neural network was reduced by residual correction,and the land subsidence prediction model was successfully applied to Xiaohe Industrial Park in Datong City,Shanxi Province.The prediction results were compared with those of the model without residual correction.The results show that SSA-Elman-RSC has higher accuracy for root mean squared error(RMSE),mean absolute error(MAE)and mean square error(MRE).The proposed model provides a new method for the prediction of land subsidence in Shanxi Province and a new idea for the establishment of combined model.