Model of gas-bearing coal permeability prediction based on APSO-WLS-SVM
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摘要: 为了较准确预测含瓦斯煤渗透率，有效预防瓦斯安全事故，提出自适应粒子群算法（APSO）优化的加权最小二乘法支持向量机（WLS-SVM）算法。根据对含瓦斯煤渗透率的相关理论及文献研究分析，选取有效应力、瓦斯压力、温度和抗压强度作为主要特征指标，采用APSO算法对WLS-SVM模型的组合参数（C、σ）寻优，建立APSO-WLS-SVM含瓦斯煤渗透率预测模型。结合现场实测资料中的40组数据作为训练样本，其余10组为预测样本，对该模型进行训练与检验，并将其预测结果与利用PSO-WLS-SVM和WLS-SVM模型的预测结果进行对比。结果表明：APSO-WLS-SVM模型的预测效果优于另外2个模型，提高了煤体渗透率的预测性能与泛化能力。
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关键词:
- 含瓦斯煤 /
- 渗透率 /
- 自适应粒子群算法(APSO) /
- 加权最小二乘法支持向量机(WLS-SVM)
Abstract: In order to predict gas-bearing coal permeability accurately and prevent gas accidents effectively, a weighted least square support vector machine(WLS-SVM) algorithm based on Adaptive Particle Swarm Optimization(APSO) is proposed. Based on the related theory and literature research on gas-bearing coal permeability, the effective stress, gas pressure, temperature and compressive strength were selected as the main characteristic indexes. The APSO algorithm was used to optimize the combination parameters(C, σ) of the WLS-SVM model and APSO-WLS-SVM gas-bearing coal permeability prediction model was established. According to the field data, 40 sets of data were used as training samples and the remaining 10 groups were predicted samples. Then the model was trained and tested, and its prediction results were compared with the results of PSO-WLS-SVM and WLS-SVM respectively. The results show that the prediction effect of APSO-WLS-SVM model is better than the other two models, improves the prediction performance and generalization ability of coal permeability. -
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