基于蚁群算法优化极限学习机模型的滑坡位移预测

Landslide displacement prediction based on extreme learning machine optimized by ant colony algorithm

采用高精度的优化算法对于提高滑坡位移预测模型的准确性具有重要意义,然而已有文献中很少对多种优化算法进行对比研究。以三峡库区的八字门滑坡为例,以极限学习机(ELM)理论为基础进行滑坡位移预测,同时运用多种算法对建立模型过程中的参数选择进行优化以期提高预测效果。为提高预测精度,以移动平均法为基础,将滑坡位移分解为趋势项和周期项,趋势项位移使用多项式函数进行预测,周期项位移使用MATLAB自编程序的极限学习机模型进行预测,两项预测值相加即可得到最终的累计位移预测值。计算结果表明:单一的ELM模型能够较为准确地预测具有阶跃式曲线的滑坡累计位移,预测结果的平均误差为23.5 mm,拟合优度为0.973。与粒子群算法和遗传算法相比,蚁群算法(ACO)在计算用时和优化效果上更优,蚁群算法优化极限学习机模型对位移的预测精度也最高,平均误差为10.1 mm,拟合优度为0.998,可在类似滑坡的位移预测研究中进行推广。

The application of optimization algorithms with high accuracy is very important for improving the accuracy of the prediction model for landslide displacement;however,the research on the comparison of different optimization algorithms is rarely reported.Here,the Bazimen landslide in the Three Gorges Reservoir area was taken as the example,and the extreme learning machine(ELM) model was used to predict the landslide displacement.Meanwhile,multiple algorithms were used to optimize the parameters in the modelling process to improve the prediction accuracy.In order to improve the prediction accuracy,based on the moving average method,the landslide displacement was decomposed into two phases,which were trend term and periodic term displacements.The trend term displacement was predicted by a polynomial function,and the ELM model that was completed by MATLAB code was used to predict the periodic term displacement.Finally,the trend and periodic displacements were summed up as the predicted total displacement.The results showed that ELM model could accurately predict the cumulative landslide displacement with a step-like curve,the average error of the prediction results was 23.5 mm and the goodness of fit was 0.973.Compared with particle swarm optimization and genetic algorithm,the ant colony optimization(ACO) performed better on computational time and calculation result.Hence,the extreme learning machine model optimized by ant colony algorithm had the best accuracy,with the average error of 10.1 mm and goodness of fit of 0.998.So,this novel model is applicable for the displacement prediction of similar landslides.