Application of time series model in water inflow prediction of working face

Authors

SHI Longqin, College of Geological Sciences & Engineering, Shandong University of Science and Technology, Qingdao 266590, China
WANG Yaru, College of Geological Sciences & Engineering, Shandong University of Science and Technology, Qingdao 266590, China
QIU Mei, College of Geological Sciences & Engineering, Shandong University of Science and Technology, Qingdao 266590, China
GAO Weifu, Shanxian Energy Co. Ltd., Feicheng Mining Group, Heze 274300, China

Abstract

In practical production of mines, the prediction of mine water inflow is of great significance for mine water prevention and control. Taking working face 1301 of Yuncheng coal mine as the research object, and without considering the influence of seasonal factors, ARIMA-the time series analysis model-is used to establish the functional relationship between mine water inflow and time, which proves that the time series of water inflow in working face 1301 of Yuncheng coal mine is affected by seasonal factors. Then, based on the principle of addition and decomposition of time series, the long-term trend, seasonal indexes, circulating factors and random parameters in the water inflow time series are separated and extracted, and the nonlinear regression correction model of water inflow prediction is established through applying the entropy method to determine the weight of each parameter. After that, the simulation results are compared with the water inflow by using ARIMA model ignoring the seasonal effect. The results show that the prediction of mine water inflow based on the non-linear time series of entropy weight is close to the measured water inflow, which verifies the accuracy of the method.

Keywords

mine water inflow prediction, time series decomposition model, ARIMA model, entropy weight, Yuncheng coal mine of Shandong

DOI

10.3969/j.issn.1001-1986.2020.03.016

Recommended Citation

SHI Longqin, WANG Yaru, QIU Mei, et al. (2020) "Application of time series model in water inflow prediction of working face," Coal Geology & Exploration: Vol. 48: Iss. 3, Article 17.
DOI: 10.3969/j.issn.1001-1986.2020.03.016
Available at: https://cge.researchcommons.org/journal/vol48/iss3/17

Reference

[1] 苗霖田,贺晓浪,张建军,等. 矿井涌水量的时间序列分析-水文地质比拟法预测[J]. 中国煤炭,2017,43(3):32-35. MIAO Lintian,HE Xiaolang,ZHANG Jianjun,et al. Time series analysis-hydrogeological analogy method for mine water inflow prediction[J]. China Coal,2017,43(3):32-35.

[2] 罗安昆,李瑾,杨建,等. 煤炭开采对干旱半干旱地区地下水资源的影响[J]. 煤矿安全,2018,49(12):216-220. LUO Ankun,LI Jin,YANG Jian,et al. Influence of coal mining on underground water resources in arid and semiarid region[J]. Safety in Coal Mines,2018,49(12):216-220.

[3] 黄存捍,冯涛,王卫军,等. 基于分形和支持向量机矿井涌水量的预测[J]. 煤炭学报,2010,35(5):806-810. HUANG Cunhan,FENG Tao,WANG Weijun,et al. Mine water inrush prediction based on fractal and support vector machines[J]. Journal of China Coal Society,2010,35(5):806-810.

[4] 陈玉华,杨永国,彭高辉. 矿井涌水量混沌时间序列分析与预测[J]. 煤田地质与勘探,2008,36(4):34-36. CHEN Yuhua,YANG Yongguo,PENG Gaohui. Chaotic time series analysis and prediction for mine water inflow[J]. Coal Geology & Exploration,2008,36(4):34-36.

[5] 凌成鹏,孙亚军,杨兰和,等. 基于BP神经网络的孔隙充水矿井涌水量预测[J]. 水文地质工程地质,2007,34(5):55-58. LING Chengpeng,SUN Yajun,YANG Lanhe,et al. Prediction of inrush water of mine with pore water yield based on BP artificial neural network[J]. Hydrogeology & Engineering Geology,2007,34(5):55-58.

[6] 武强,崔芳鹏,赵苏启,等. 矿井水害类型划分及主要特征分析[J]. 煤炭学报,2013,38(4):561-565. WU Qiang,CUI Fangpeng,ZHAO Suqi,et al. Type classification and main characteristics of mine water disasters[J]. Journal of China Coal Society,2013,38(4):561-565.

[7] 类超. 矿坑涌水量预测结果的风险率估算模型研究[D]. 合肥:合肥工业大学,2018. LEI Chao. Study on the risk estimation model of prediction results of mine water inflow[D]. Hefei:Hefei University of Technology,2018.

[8] 王猛,殷博超,张凯歌,等. 基于ARIMA乘积季节模型的矿井涌水量预测研究[J]. 煤炭科学技术,2017,45(11):199-204. WANG Meng,YIN Bochao,ZHANG Kaige,et al. Study on prediction of mine water inflow volume based on ARIMA product seasonal model[J]. Coal Science and Technology,2017,45(11):199-204.

[9] 安欣,贾进章. 矿井涌水量时间序列ARIMA预测模型[J]. 辽宁工程技术大学学报(自然科学版),2015,34(7):785-790. AN Xin,JIA Jinzhang. Time series prediction of mine water inflow of ARIMA model[J]. Journal of Liaoning Technical University(Natural Science),2015,34(7):785-790.

[10] 刘学刚,张腾飞,韩印. 基于ARIMA模型的短时交通流预测研究[J]. 物流科技,2019,42(12):91-94. LIU Xuegang,ZHANG Tengfei,HAN Yin. Research on short-term traffic flow forecast based on ARIMA model[J]. Logistics Sci-Tech,2019,42(12):91-94.

[11] 尹义星,许有鹏,张小娜,等. 基于时间序列分解方法的太湖未来特征水位预测[J]. 水土保持通报,2012,32(4):172-175. YIN Yixing,XU Youpeng,ZHANG Xiaona,et al. Forecasting characteristic water levels of Taihu lake based on time series decomposing method[J]. Bulletin of Soil and Water Conservation,2012,32(4):172-175.

[12] 张丽霞,雷晓云. 时间序列分解模型在乌拉斯台河年径流量预测中的应用研究[J]. 水资源与水工程学报,2006,17(2):22-24. ZHANG Lixia,LEI Xiaoyun. Application of the time series decomposable model for the annual runoff prediction in Wulasitai river[J]. Journal of Water Resources and Water Engineering,2006,17(2):22-24.

[13] 李志亮,罗芳,张天津. 一种基于多层次分析和熵权的灰色聚类评价方法[J]. 延边大学学报(自然科学版),2017,43(4):321-326. LI Zhiliang,LUO Fang,ZHANG Tianjin. A gray clustering evaluation method based on multi-level analysis and entropy weight[J]. Journal of Yanbian University(Natural Science),2017,43(4):321-326.

[14] 胡友彪,邢世平,张淑莹. 基于可拓模型判别矿井突水水源[J]. 安徽理工大学学报(自然科学版),2017,37(6):34-40. HU Youbiao,XING Shiping,ZHANG Shuying. Identifying source of mine water inrush based on extension model[J]. Journal of Anhui University of Science and Technology(Natural Science),2017,37(6):34-40.

[15] 潘国营,杜鹏卓,陈国胜. 基于EW-FAHP的煤层底板承压水突水危险评价[J]. 水文地质工程地质,2017,44(1):131-136. PAN Guoying,DU Pengzhuo,CHEN Guosheng. Risk evaluation of confined water-inrush from coal seam floor based on EW-FAHP[J]. Hydrogeology & Engineering Geology,2017,44(1):131-136.

[16] 秦永泰. 基于FAHP-EWM的煤层底板突水脆弱性评价:以平顶山五矿为例[D]. 焦作:河南理工大学,2016. QIN Yongtai. Vulnerability assessment of coal floor groundwater bursting based on FAHP-EWM:A case study in the fifth coal mine mine of Pingdingshan Mining Group[D]. Jiaozuo:Henan Polytechnic University,2016.

[17] 何保,李振南,赵世杰. 基于多元非线性回归分析的露天煤矿涌水量预测[J]. 煤炭科学技术,2018,46(5):125-129. HE Bao,LI Zhennan,ZHAO Shijie. Prediction on water inflow of surface mine based on multi-element nonlinear regression analysis[J]. Coal Science and Technology,2018,46(5):125-129.

[18] 赵美成,贺安民,屈世甲. 综采工作面瓦斯数据时间序列预测方法研究[J]. 工矿自动化,2019,45(6):80-85. ZHAO Meicheng,HE Anmin,QU Shijia. Research on time series prediction method of gas data on fully mechanized mining face[J]. Industry and Mine Automation,2019,45(6):80-85.

[19] 于寄语. 时间序列的结构变化和单位根检验[D]. 武汉:华中科技大学,2017. YU Jiyu. A dissertation submitted in partial fulfillment of the requirements for the degree of doctor of philosophy in economics[D]. Wuhan:Huazhong University of Science and Technology,2017

[20] 王海波. 二代小波变换在地震信号降噪技术中的应用[D]. 大庆:东北石油大学,2011. WANG Haibo. The application of the second-generation wavelet transform in denoising of the seismic data[D]. Daqing:Northeast Petroleum University,2011.

[21] 池其才,周世健,王奉伟. 基于时间序列的变形监测数据处理方法比较研究[J]. 测绘与空间地理信息,2015,38(7):193-195. CHI Qicai,ZHOU Shijian,WANG Fengwei. Deformation monitor data processing meth on based on time series comparative[J]. Geomatics & Spatial Information Technology,2015,38(7):193-195.

[22] 张培文,王晓东. 基于趋势分解的机场季节旅客吞吐量预测方法[J]. 科技和产业,2013,13(5):44-46. ZHANG Peiwen,WANG Xiaodong. An analyzing method of airport seasonal passenger transport demand based on superimposing trend[J]. Science Technology and Industry,2013,13(5):44-46.

[23] 王佩璐. 预测矿井涌水量的时间序列和BP神经网络耦合模型研究[D]. 焦作:河南理工大学,2012. WANG Peilu. Research on the coupling mine hydraulic discharge prediction model of time series and BP neural network[D]. Jiaozuo:Henan Polytechnic University,2012.