Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (3): 108-113.doi: 10.6040/j.issn.1672-3961.0.2017.449

• Mechanical Engineering • Previous Articles     Next Articles

Prediction method of tailing dam groundwater levels based on improved PSO-BP neural network

Diankun ZHENG1(),Tongle XU1,*(),Zhaojie YIN1,Qingmin MENG2   

  1. 1. Mechanical Engineering School, Shandong University of Technology, Zibo 255049, Shandong, China
    2. Shanbo Anjifu Gear Motor Co., Ltd., Zibo 255200, Shandong, China
  • Received:2017-09-04 Online:2019-06-20 Published:2019-06-27
  • Contact: Tongle XU E-mail:zdk5287@163.com;xutongle@163.com
  • Supported by:
    山东省自然科学基金资助项目(ZR2013FM005);淄博市科学技术发展计划资助项目(JY20151587)

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Abstract:

To solve the low convergence speed and poor precision problem of the traditional prediction algorithm, an improved particle swarm optimization(IPSO) algorithm was proposed. The inertia factor ω and the accelerating factor c1 and c2 of the algorithm were dynamically adjusted during the searching process to improve the optimization effectiveness. The weights and thresholds of back propagation(BP) network were optimized by the improved algorithm. And the prediction model of groundwater levels in tailing dam was built and verified according to its instance data. The test results showed that the convergence speed of algorithm and the accuracy of prediction model was improved.

Key words: dynamic optimization, groundwater levels, particle swarm, BP neural network, prediction model

CLC Number: 

  • TD76

Fig.1

The fitness curves of two algorithms"

Fig.2

Flowchart of the improved PSO-BP network prediction model"

Table 1

The instance data of tailing dam"

序号 最小干滩长度/m 库水位/m 渗流量/(m3/h) 水平位移/m 垂直位移/m 孔隙压力比 地下水位/m
1 287.36 12.35 7.50 0.012 0.005 0.40 8.54
2 289.17 12.30 7.52 0.012 0.005 0.35 8.53
3 292.14 12.18 7.48 0.012 0.005 0.25 8.43
118 285.43 12.48 7.60 0.016 0.008 0.55 8.70
119 285.04 12.56 7.68 0.016 0.008 0.58 8.77
120 287.43 12.42 7.55 0.016 0.008 0.40 8.58

Table 2

The correlation analysis result of each measuring point data and the groundwater level"

参数 最小干滩长度 库水位 渗流量 水平位移 垂直位移 孔隙压力比
相关系数r -0.832 0.901 0.811 0.103 0.072 0.863

Table 3

The test results of various neural network models with different numbers of hidden nodes"

节点数 3 4 5 6 7 8 9 10 11 12 13 14
PSO-BP 0.803 5 0.301 7 0.120 6 0.078 1 0.035 4 0.086 5 0.271 6 0.372 1 0.591 3 0.644 5 0.711 5 0.761 1
IPSO-BP 0.131 2 0.045 6 0.017 1 0.006 8 0.009 8 0.011 9 0.023 6 0.054 1 0.073 3 0.081 8 0.096 7 0.103 2

Fig.3

The prediction results of different network structure models"

Table 4

Output and error analysis results of the prediction model"

样本序号 实测值 网络结构:4-6-1 网络结构:4-7-1
PSO预测值/m PSO误差值/% IPSO预测值/m IPSO误差值/% PSO预测值/m PSO误差值/% IPSO预测值/m IPSO误差值/%
1 9.15 9.27 1.30 9.09 -0.66 9.29 1.50 9.21 0.66
2 9.10 9.19 0.99 9.05 -0.55 8.98 -1.30 9.03 -0.77
3 8.89 8.79 -1.10 8.91 0.22 8.99 1.10 8.98 1.00
4 8.80 8.86 0.68 8.76 -0.45 8.94 1.60 8.76 -0.45
5 8.85 8.92 0.79 8.85 0.00 8.96 1.20 8.81 -0.45
6 8.78 8.89 0.11 8.76 -0.23 8.69 -1.00 8.85 0.80
7 8.75 8.85 1.10 8.71 -0.46 8.82 0.80 8.71 -0.46
8 8.70 8.69 -0.11 8.72 0.23 8.78 0.92 8.75 0.57
9 8.77 8.89 1.40 8.76 -0.11 8.90 1.50 8.85 0.91
10 8.58 8.72 1.60 8.61 0.35 8.76 2.10 8.66 0.93
平均相对误差/% 0.92 0.33 1.30 0.70
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