基于智能算法优化支持向量机模型的滑坡稳定性预测

Prediction of landslide stability based on SVM model optimized by intelligent algorithm

影响滑坡稳定性的因素较多,利用滑坡稳定性影响因素快速预测滑坡稳定状态是当前滑坡研究的重要内容。利用相关系数、支持向量机、交叉验证法、遗传算法、粒子群优化算法等理论建立支持向量机模型对滑坡稳定性进行了研究。以湖北竹溪县197个滑坡为例,研究结果表明:遗传算法优化的支持向量机滑坡稳定性预测模型预测效果最好,与实际情况吻合得最好。最佳参数c为3.001 6、g为0.041 008,训练集滑坡稳定性预测的正确率为84%,测试集滑坡稳定性预测的正确率为79.32%。因此所提遗传算法优化的支持向量机滑坡稳定性预测模型对于滑坡稳定性分析具有一定参考价值。

There are many factors affecting the stability of landslide, predicting the stability state of landslide rapidly through the influence factors of landslide stability is the important part of the study of landslide. Based on use of the correlation coefficient, support vector machines, cross - validation method, genetic algorithm, particle swarm optimi- zation algorithm theories, this paper establishes support vector machine model to study the stability of landslide. Tak- ing 197 landslides in Zhuxi County, Hebei Province as examples, the results show that the genetic algorithm optimi- zation support vector machine landslide stability prediction model works best, and best matches with the actual situ- ation. Optimal parameter e is 3. 0016, g is 0. 041008, the training set landslide stability prediction correct rate is 84%, the test set landslide stability prediction correct rates is 79.3196%. Therefore, genetic algorithm optimization＇s Support Vector Machine landslide stability prediction model has certain reference value for landslide stability analysis.