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[1]丁莹莹,尹尚先,连会青,等.基于CEEMDAN和改进的混合时间序列模型工作面涌水量预测研究*[J].中国安全生产科学技术,2024,20(3):110-117.[doi:10.11731/j.issn.1673-193x.2024.03.016]
　DING Yingying,YIN Shangxian,LIAN Huiqing,et al.Research on water inflow prediction of working face based on CEEMDAN and improved hybrid time series model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(3):110-117.[doi:10.11731/j.issn.1673-193x.2024.03.016]
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基于CEEMDAN和改进的混合时间序列模型工作面涌水量预测研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年3期
页码:
110-117
栏目:
职业安全卫生管理与技术
出版日期:
2024-03-31

文章信息/Info

Title:
Research on water inflow prediction of working face based on CEEMDAN and improved hybrid time series model
文章编号:
1673-193X(2024)-03-0110-08
作者:
丁莹莹; 尹尚先; 连会青; 卜昌森; 刘伟; 夏向学; 周旺
（1.华北科技学院 安全工程学院，河北 廊坊 065201；
2.吉林大学 建设工程学院，吉林 长春 130026；
3.山西汾西矿业（集团）有限责任公司 矿山救护大队，山西 孝义 032308）
Author(s):
DING Yingying;  YIN Shangxian;  LIAN Huiqing;  BU Changsen;  LIU Wei;  XIA Xiangxue;  ZHOU Wang
(1.School of Safety Engineering,North China University of Science and Technology,Langfang Hebei 065201,China;
2.School of Construction Engineering,Jilin University,Changchun Jilin 130026,China;
3.Mine Rescue Brigade,Shanxi Fenxi Mining (Group) Co.,LTD.,Xiaoyi Shanxi 032308,China)
关键词:
涌水量预测; 时间序列预测; 混合模型; 经验模态分解; 麻雀搜索算法
Keywords:
water inflow prediction;  time series prediction;  hybrid model;  empirical mode decomposition (EMD);  sparrow search algorithm (SSA)
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.03.016
文献标志码:
A
摘要:
为提高采煤工作面涌水量预测准确度，收集大量工作面涌水量观测数据进行整理、统计、分析，将涌水量稳定性、周期性和季节性特征考虑在内，提出1种基于数据驱动的完全自适应模态分解算法（CEEMDAN）和改进的混合时间序列模型工作面涌水量预测方法。该方法利用CEEMDAN处理涌水量数据，构建麻雀搜索算法（SSA）优化的长短期记忆网络（LSTM）和自回归移动平均模型（ARIMA）并行级联而成的混合时间序列模型对工作面涌水量进行预测。研究结果表明：该模型预测结果与真实数据相差更小，平均绝对误差为6.36 m3/h，均方根误差为10.6 m3/h，模型拟合系数为0.95，更适用于工作面涌水量预测。研究结果可为矿井工作面涌水量预测及防控提供参考。
Abstract:
In order to improve the prediction accuracy of water inflow in coal mining face,a large number of observation data of water inflow in coal mining face were collected for collation,statistics and analysis.Taking into account the stability,periodicity and seasonal characteristics of water inflow,a prediction method of water inflow in working face based on the data-driven adaptive noise-complete set empirical mode decomposition algorithm (CEEMDAN) and the hybrid time series model was proposed.In this method,the water inflow data was processed by using CEEMDAN,and a hybrid time series model formed by the parallel concatenation of long short-term memory network (LSTM) optimized by sparrow search algorithm (SSA) and autoregressive integrated moving average model (ARIMA) was constructed to predict the water inflow of working face.The results show that the difference between the prediction results of the hybrid model and the real data is smaller,and it is more suitable for the prediction of water inflow in working face.The average absolute error of the model prediction results is reduced to 6.36 m3/h,the root mean square error is reduced to 10.6 m3/h,and the model fit coefficients are 0.95,which not only overcomes the interference of other related influencing factors,but also improves the prediction accuracy and speeds up the prediction speed.The research results can provide a reference for the prediction and prevention of water inflow in mine working faces.

参考文献/References:

［1］ 徐慧,牟义,牛超.矿井涌水量渗透系数构造分形优化研究［J］.煤炭科学技术,2021,49(10):228-232.XU Hui,MOU Yi,NIU Chao.Study on optimization of permeability coefficient of structural fractal for mine water inflow［J］.Coal Science and Technology,2021,49(10):228-232.
［2］ 侯恩科,席慧琴,文强,等.基于GMS的隐伏火烧区下煤层开采工作面涌水量预测［J］.安全与环境学报,2022,22(5):2482-2492.HOU Enke,XI Huiqin,WEN Qiang,et al.Prediction of water inflow volume in the coal mining workforce below the concealed fire area based on GMS［J］.Journal of Safety and Environment,2022,22(5):2482-2492.
［3］ 王璐.基于遗传算法和支持向量机的西南岩溶越岭隧道涌水量预测［D］.成都：成都理工大学,2019.
［4］ 杨建,王皓,梁向阳,等.鄂尔多斯盆地北部深埋煤层工作面涌水量预测方法［J］.煤田地质与勘探,2021,49(4):185-191.YANG Jian,WANG Hao,LIANG Xiangyang,et al.Water inflow forecasting method of deep buried coal working face in Northern Ordos Basin［J］.Coal Geology & Exploration,2021,49(4):185-191.
［5］ 吴卫忠,邓忠,陈余道,等.广西盘龙铅锌矿涌水量时间序列变化特征分析及ARIMA预测［J］.桂林理工大学学报,2023,43(3):406-413.WU Weizhong,DENG Zhong,CHEN Yudao,et al.Characteristic analysis and ARIMA prediction of water inflow time series for Panlong lead-zinc deposit in Guangxi［J］.Journal of Guilin University of Technology,2023,43(3):406-413.
［6］ 施龙青,王雅茹,邱梅,等.时间序列模型在工作面涌水量预测中的应用［J］.煤田地质与勘探,2020,48(3):108-115,121.SHI Longqing,WANG Yaru,QIU Mei,et al.Application of time series model in prediction of water inflow in working face［J］.Coal Geology & Exploration,2020,48(3):108-115,121.
［7］ 刘慧,刘桂芹,宁殿艳,等.基于VMD-DBN的矿井涌水量预测方法［J］.煤田地质与勘探,2023,51(6):13-21.LIU Hui,LIU Guiqin,NING Dianyan,et al.Mine water inflow prediction method based on VMD-DBN model［J］.Coal Geology & Exploration,2023,51(6):13-21.
［8］ 梁满玉,尹尚先,姚辉,等.基于DRN-BiLSTM模型的矿井涌水量预测［J］.煤矿安全,2023,54(5):56-62.LIANG Manyu,YIN Shangxian,YAO Hui,et al.Mine water inflow prediction based on DRN-BiLSTM model［J］.Safety in Coal Mines,2023,54(5):56-62.
［9］ 李克文,于明洋.基于双重注意力机制改进的DA-LSTM时间序列预测模型［J］.计算机应用与软件,2023,40(2):253-258,349.LI Kewen,YU Mingyang.An improved DA-LSTM time series prediction model based on dual attention mechanism［J］.Computer Applications and Software,2023,40(2):253-258,349.
［10］ 王怡,普运伟.基于CNN-BiLSTM-Attention融合神经网络的大气温度预测［J］.中国水运,2023,23(1):25-27.WANG Yi,PU Yunwei.Atmospheric temperature prediction based on CNN-BiLSTM-Attention fusion neural network［J］.China Water Transport,2023,23(1):25-27.
［11］ 罗灿坤,刘昊,黄鑫,等.基于VMD-LSTM-ARMA模型的径流预测［J］.人民珠江,2023,44(4):96-102.LUO Cankun,LIU Hao,HUANG Xin,et al.Run off prediction model based on the VMD-LSTM-ARMA algorithm［J］.Pearl River,2023,44(4):96-102.
［12］ 冷丽英,付建哲,宁波.基于SSA-LSTM模型的IGBT时间序列预测研究［J］.半导体技术,2023,48(1):66-72.LENG Liying,FU Jianzhe,Ning Bo.Research on IGBT sequentially prediction based on SSA-LSTM model［J］.Semiconductor Technology,2023,48(1):66-72.
［13］ YAO D,CHEN S,DONG S,et al.Modeling abrupt changes in mine water inflow trends:a CEEMDAN-based multi-model prediction approach［J］.Journal of Cleaner Production,2024,439:140809.
［14］ 张泽龙,韦冬妮,唐梦媛,等.基于改进LSTM的电力负荷预测与成本感知优化策略研究［J］.电子设计工程,2023,31(21):132-136.ZHANG Zelong,WEI Dongni,TANG Mengyuan,et al.Research on power load forecasting and cost perception optimization strategy based on improved LSTM［J］.Electronic Design Engineering,2023,31(21):132-136.
［15］ 邵良杉,毕圣昊,王彦彬,等.基于ISSA-ELM的煤与瓦斯突出危险等级预测［J］.中国安全生产科学技术,2023,19(9):76-82.SHAO Liangshan,BI Shenghao,WANG Yanbin,et al.Prediction of coal and gas outburst risk level based on ISSA-ELM［J］.Journal of Safety Science and Technology,2023,19(9):76-82.
［16］ 龙宇,许浩然,余华云,等.基于ARIMA-LSTM-XGBoost组合模型的铁路货运量预测［J］.科学技术与工程,2023,23(25):10879-10886.LONG Yu,XU Haoran,YU Huayun，et al.Prediction of railway freight volume based on ARIMA-LSTM-XGBoost combination model［J］.Science Technology and Engineering,2023,23(25):10879-10886.

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备注/Memo

备注/Memo:
收稿日期： 2023-10-14
* 基金项目： 国家自然科学基金项目（42202291）；中央高校基本科研业务费专项资金资助项目（3142021004,3142022003）
作者简介： 丁莹莹，硕士研究生，主要研究方向为应急信息化技术、水文地质学。
通信作者： 尹尚先，博士，教授，主要研究方向为煤矿防治水。
更新日期/Last Update: 2024-04-07