基于IWOA−SVM的煤矿突水预测模型

秋兴国; 李靖

doi:10.13272/j.issn.1671-251x.2021050043

基于IWOA−SVM的煤矿突水预测模型

doi: 10.13272/j.issn.1671-251x.2021050043

秋兴国,
李靖

西安科技大学计算机科学与技术学院, 陕西西安　710600

基金项目: 陕西省自然科学基础研究资助项目(2019JM-348)。

详细信息

作者简介:
秋兴国(1964－)，男，陕西乾县人，教授，研究方向为计算机监测与控制、智能信息处理、煤矿灾害监测预报，E-mail: sxxykj@126.com

中图分类号: TD745
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-18
- 修回日期: 2022-01-07
- 刊出日期: 2022-01-20

Prediction model of water inrush in coal mine based on IWOA-SVM

College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710600, China

摘要

摘要: 针对传统煤矿突水预测算法易陷入局部最优、预测结果准确率低及速度慢等问题，提出一种基于改进鲸鱼优化算法(IWOA)−支持向量机(SVM)的煤矿突水预测模型。IWOA从鲸鱼种群初始化、调节因子非线性化及随机差分进化(DE)3个方面入手对鲸鱼优化算法(WOA)进行改进：使用Tent映射初始化鲸鱼种群，提高鲸鱼种群寻找到最优猎物的可能性；通过调节因子非线性变化策略，提升算法在迭代前期的全局搜索能力及迭代后期的局部搜索能力，从而加快收敛速度；引入DE算法的变异、交叉、选择操作，以增强WOA的全局搜索能力。利用IWOA对SVM模型进行参数优化，将影响煤矿突水的水压、隔水层厚度、煤层倾角、断层落差、断层与工作面距离、采高共6个影响因素作为模型的输入特征向量，突水与安全2种突水结果作为模型的输出向量，以突水预测结果与实际结果间的误差最小化为目标建立目标函数，得到基于IWOA−SVM的煤矿突水预测模型。实验结果表明：与粒子群优化算法、DE算法、WOA相比，IWOA的预测准确率最高，标准误差最小，且收敛速度快，鲁棒性好；IWOA−SVM的突水预测准确率达到100%，与传统的突水系数法、SVM、WOA−SVM相比，IWOA−SVM表现出更高的准确率和稳定性。
- 煤矿突水预测 /
- 鲸鱼优化 /
- 支持向量机 /
- 差分进化 /
- 混沌映射
Abstract: The traditional prediction algorithm of water inrush in coal mine is easy to fall into local optimum, the prediction results accuracy is low and the speed is slow. In order to solve the above problems, a prediction model of water inrush in coal mine based on improved whale optimization algorithm (IWOA) and support vector machine (SVM) is proposed. IWOA improves the whale optimization algorithm (WOA) from three aspects, whale population initialization, nonlinear adjustment factor and random differential evolution (DE). Tent mapping is used to initialize the whale population to improve the possibility of the whale population finding the optimal prey. The non-linear change strategy of the adjustment factor is applied to improve the global search capability of the algorithm in the early stage of the iteration and the local search capability in the later stage of the iteration so as to speed up the convergence speed. The mutation, crossover and selection operations of DE algorithm are introduced to enhance the global search capability of WOA. The parameters of SVM model are optimized by IWOA. The six factors affecting water inrush in coal mine, including water pressure, thickness of aquiclude, dip angle of coal seam, fault drop, distance between fault and working face and mining height are taken as the input characteristic vectors of the model. The two water inrush results of water inrush and safety are taken as the output vectors. The objective function is established to minimize the error between the water inrush prediction results and the actual results, and the coal mine water inrush prediction model based on IWOA−SVM is obtained. The experimental results show that IWOA has the highest prediction accuracy, minimum standard error, fast convergence and good robustness compared with particle swarm optimization, DE algorithm and WOA. The accuracy of water inrush prediction of IWOA−SVM is 100%. Compared with the traditional water inrush coefficient method, SVM and WOA−SVM, IWOA−SVM shows higher accuracy and stability.
- prediction of water inrush in coal mine /
- whale optimization /
- support vector machine /
- differential evolution /
- chaotic mapping

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图 1 基于IWOA−SVM的煤矿突水预测流程

Figure 1. Water inrush prediction flow based on IWOA−SVM

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图 2 4种算法的收敛曲线

Figure 2. Convergence curves of four algorithms

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图 3 IWOA−SVM预测结果

Figure 3. IWOA−SVM prediction results

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表 1 归一化处理后的部分突水数据

Table 1. Partial water inrush data after normalization

样本编号	水压z₁	隔水层厚度z₂	煤层倾角z₃	断层落差z₄	断层与工作面距离z₅	采高z₆	突水状态
1	0.383 4	0.199 4	0.466 7	0.080 0	0.123 1	0	1
2	0.339 4	0.188 5	0.800 0	1.000 0	0.692 3	0.111 1	1
7	0.256 4	0.400 7	0.133 3	0.016 0	0.476 9	0.006 9	0
8	0.172 3	0.258 0	0.533 3	0.064 0	0.061 5	0.090 3	1
12	0	0.325 5	0.066 7	0.440 0	0.046 2	0.013 9	0
19	0.158 0	0.209 4	0.066 7	0.020 0	0.423 1	0.090 3	0
25	0.497 4	0.453 5	0.333 3	0.035 0	0.061 5	0.152 8	1
32	0.196 9	0.070 6	0.400 0	0.300 0	0.123 1	0.111 1	1
34	0.717 6	0.560 4	0.400 0	0.070 0	0.092 3	0.251 4	1
42	0.300 5	0.451 5	0.566 7	0.024 0	0.053 8	0.097 2	0
43	0.373 1	0.451 5	0.600 0	0.220 0	0.276 9	0.097 2	0

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表 2 4种算法的寻优结果

Table 2. Optimizing results of four algorithms

算法	寻优结果		准确率/%	标准误差	收敛代数
算法	c	σ	准确率/%	标准误差	收敛代数
PSO	4.358	0.010	60.47	0.489	100
DE	7.208	0.325	83.72	0.386	29
WOA	29.136	0.066	81.39	0.408	43
IWOA	6.517	0.418	88.37	0.333	8

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表 3 不同方法预测结果比较

Table 3. Comparison of prediction results of different methods

序号	实际结果	预测结果
序号	实际结果	突水系数法	SVM	WOA−SVM	IWOA−SVM
44	0	0	1	0	0
45	1	0	1	1	1
46	0	0	0	0	0
47	0	0	0	0	0
48	0	0	0	0	0
49	1	1	1	1	1
50	0	0	1	0	0
51	1	0	1	0	1
52	1	0	1	1	1
53	0	0	0	0	0
54	1	1	1	1	1
55	1	1	1	1	1

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