ISSN 1000-3665 CN 11-2202/P
  • Included in Scopus
  • Included in DOAJ
  • Included in WJCI Report
  • Chinese Core Journals
  • The Key Magazine of China Technology
  • Included in CSCD
Volume 50 Issue 2
Mar.  2023
Turn off MathJax
Article Contents
LI Zhichao, JIANG Baoliang, PAN Deng, et al. Application of the grey theory to dynamic analyses of the Baiquan Spring flow rate in Xinxiang[J]. Hydrogeology & Engineering Geology, 2023, 50(2): 34-43 doi:  10.16030/j.cnki.issn.1000-3665.202205047
Citation: LI Zhichao, JIANG Baoliang, PAN Deng, et al. Application of the grey theory to dynamic analyses of the Baiquan Spring flow rate in Xinxiang[J]. Hydrogeology & Engineering Geology, 2023, 50(2): 34-43 doi:  10.16030/j.cnki.issn.1000-3665.202205047

Application of the grey theory to dynamic analyses of the Baiquan Spring flow rate in Xinxiang

doi: 10.16030/j.cnki.issn.1000-3665.202205047
  • Received Date: 2022-05-18
  • Rev Recd Date: 2022-07-15
  • Available Online: 2023-03-14
  • Publish Date: 2023-03-15
  • The Baiquan Spring in Xinxiang has many functions, such as water supply, agricultural irrigation, humanities, tourism and ecology. It is of great significance to study the dynamics of the spring flow rate and establish a dynamic prediction model for the spring water resources evaluation and protection. In order to further study the dynamic characteristics of the Baiquan Spring flow rate in Xinxiang and evaluate karst water resources in the spring area, based on the data of annually measured spring flow rate and annual average precipitation in the spring area from 1964 to 1978, the main influencing factors of the spring flow rate are determined by using the stepwise regression analysis, and a stepwise regression model is established, with remarkable regression effect. On the basis of the stepwise regression analysis, this paper establishes the GM(1, 2) model, NSGM(1, 2) model and GM(0, 2) model for the dynamic prediction of the spring flow rate. The results show that (1) from 1964 to 1978, the Baiquan spring flow rate was mainly controlled by the precipitation in the spring area, and the spring flow rate lagged behind the precipitation for one year, reflecting the dynamic characteristics of the spring water in the natural state. (2) The accuracy levels of the three grey models are the highest (excellent). (3) From 1964 to 1978, the measured discharge of the Baiquan spring ranged from 2.347 to 6.448 m3/s, with an average of 3.904 m3/s. The predicted values of the stepwise regression model range from 1.882 to 6.383 m3/s, with an average of 3.904 m3/s. The predicted value of the GM(1, 2) model varies between 2.327 and 6.448 m3/s, with an average of 3.939 m3/s. The predicted values of the NSGM(1, 2) model range from 2.133 to 6.448 m3/s, with an average of 3.927 m3/s. The predicted values of the GM(0, 2) model range from 1.787 to 6.448 m3/s, with an average of 3.907 m3/s. (4) The average relative errors of the stepwise regression model and the three grey models mentioned above are 7.794%, 7.292%, 7.122% and 7.797% respectively, all of which are less than 10%, indicating that they can be used for dynamic prediction of the spring water. Among them, the NSGM(1, 2) model has a higher accuracy and better fitting to the inflection point of the curve. (5) According to the spring flow rate from 1964 to 2030 predicted by the four models, the exploitation resources of the karst water in the Baiquan spring area should not exceed 1.69 m3/s from the angle of spring protection. The research results can not only provide scientific basis for spring flow dynamic prediction and spring area water resources evaluation, but also provide reference for the study of groundwater dynamics in similar areas.
  • loading
  • [1]
    LIANG Yongping,GAO Xubo,ZHAO Chunhong,et al. Review:Characterization,evolution,and environmental issues of Karst water systems in Northern China[J]. Hydrogeology Journal,2018,26(5):1371 − 1385. doi:  10.1007/s10040-018-1792-4
    梁永平,王维泰,赵春红,等. 中国北方岩溶水变化特征及其环境问题[J]. 中国岩溶,2013,32(1):34 − 42. [LIANG Yongping,WANG Weitai,ZHAO Chunhong,et al. Variations of Karst water and environmental problems in North China[J]. Carsologica Sinica,2013,32(1):34 − 42. (in Chinese with English abstract) doi:  10.3969/j.issn.1001-4810.2013.01.006
    梁永平,申豪勇,赵春红,等. 对中国北方岩溶水研究方向的思考与实践[J]. 中国岩溶,2021,40(3):363 − 380. [LIANG Yongping,SHEN Haoyong,ZHAO Chunhong,et al. Thinking and practice on the research direction of Karst water in Northern China[J]. Carsologica Sinica,2021,40(3):363 − 380. (in Chinese with English abstract)
    王志恒, 梁永平, 申豪勇, 等. 自然与人类活动叠加影响下晋祠泉域岩溶地下水动态特征[J]. 吉林大学学报(地球科学版),2021,51(6):1823 − 1837. [WANG Zhiheng,LIANG Yongping,SHEN Haoyong,et al. Dynamic characteristics of karst groundwater in Jinci Spring under superimposed influence of natural and human activities[J]. Journal of Jilin University (Earth Science Edition),2021,51(6):1823 − 1837. (in Chinese with English abstract)
    HAO Yonghong,ZHANG Juan,WANG Jiaojiao,et al. How does the anthropogenic activity affect the spring discharge?[J]. Journal of Hydrology,2016,540:1053 − 1065. doi:  10.1016/j.jhydrol.2016.07.024
    王大伟,乔小娟,高波,等. 山西龙子祠岩溶泉流量动态特征与影响因素分析[J]. 中国岩溶,2021,40(3):420 − 429. [WANG Dawei,QIAO Xiaojuan,GAO Bo,et al. Dynamic characteristics and influence factors of discharge of the Longzici Karst spring in Shanxi Province[J]. Carsologica Sinica,2021,40(3):420 − 429. (in Chinese with English abstract)
    LIU Y,WANG B,ZHAN H,et al. Simulation of nonstationary spring discharge using time series models[J]. Water Resources Management,2017,31(15):4875 − 4890. doi:  10.1007/s11269-017-1783-6
    茅伟绩,王锦国. 云南鹤庆县蝙蝠洞泉流量衰减分析[J]. 中国煤炭地质,2021,33(11):47 − 50. [MAO Weiji,WANG Jinguo. Analysis of Bianfudong spring flow attenuation in Heqing County,Yunnan Province[J]. Coal Geology of China,2021,33(11):47 − 50. (in Chinese with English abstract) doi:  10.3969/j.issn.1674-1803.2021.11.09
    梁日胜,曾成,闫志为,等. 贵州印江朗溪岩溶槽谷龙洞湾泉流量衰减分析[J]. 中国岩溶,2019,38(1):10 − 18. [LIANG Risheng,ZENG Cheng,YAN Zhiwei,et al. Recession flow analysis for Longdongwan spring at Langxi Karst valley in Yinjiang County,Guizhou Province[J]. Carsologica Sinica,2019,38(1):10 − 18. (in Chinese with English abstract)
    WU Xiancang,LI Changsuo,SUN Bin,et al. Groundwater hydrogeochemical formation and evolution in a Karst aquifer system affected by anthropogenic impacts[J]. Environmental Geochemistry and Health,2020,42(9):2609 − 2626. doi:  10.1007/s10653-019-00450-z
    NUNNO D F,GRANATA F. Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network[J]. Environmental Research,2020,190:110062. doi:  10.1016/j.envres.2020.110062
    HAO Yonghong,YEH T C J,GAO Zongqiang,et al. A gray system model for studying the response to climatic change:The Liulin Karst springs,China[J]. Journal of Hydrology,2006,328(3/4):668 − 676.
    HAO Yonghong,CAO Bibo,CHEN Xiang,et al. A piecewise grey system model for study the effects of anthropogenic activities on Karst hydrological processes[J]. Water Resources Management,2013,27(5):1207 − 1220. doi:  10.1007/s11269-012-0231-x
    LIU Peigui,GAO Yang,SHANG Manting,et al. Predicting water level rises and their effects on surrounding Karst water in an abandoned mine in Shandong,China[J]. Environmental Earth Sciences,2020,79(1):1 − 10. doi:  10.1007/s12665-019-8746-6
    AN Lixing,HAO Yonghong,YEH T C J,et al. Simulation of Karst spring discharge using a combination of time-frequency analysis methods and long short-term memory neural networks[J]. Journal of Hydrology,2020,589:125320. doi:  10.1016/j.jhydrol.2020.125320
    CHENG Shu,QIAO Xiaojun,SHI Yaolin,et al. Machine learning for predicting discharge fluctuation of a karst spring in North China[J]. Acta Geophysica,2021,69:257 − 270.
    林云,曲鹏冲,吕海新,等. 太行山东缘典型岩溶泉流量变化特征及规律分析[J]. 中国岩溶,2018,37(5):671 − 679. [LIN Yun,QU Pengchong,LV Haixin,et al. Variation characteristics of typical karst springs in the eastern margin of the Taihang Mountains[J]. Carsologica Sinica,2018,37(5):671 − 679. (in Chinese with English abstract)
    卢丽,邹胜章,赵一,等. 桂林会仙湿地狮子岩地下河系统水循环对降水的响应[J]. 水文地质工程地质,2022,49(5):63 − 72. [LU Li,ZHOU Shengzhang,ZHAO Yi,et al. Response of water cycle to precipitation in Shizhiyan underground river system in Huixian wetland of Guilin[J]. Hydrogeology & Engineering Geology,2022,49(5):63 − 72. (in Chinese with English abstract)
    郭艺,王枫,甘甫平,等. 基于移动平均模型和指数平滑模型的岩溶泉泉流量预测[J]. 河北地质大学学报,2020,43(4):19 − 25. [GUO Yi,WANG Feng,GAN Fuping,et al. Forecasting of spring flow based on moving average model and exponential smoothing model[J]. Journal of Hebei GEO University,2020,43(4):19 − 25. (in Chinese with English abstract)
    姜宝良,付北锋,赵延涛. 泉水动态分析预测和资源评价:以辉县百泉为例[J]. 水文地质工程地质,2002,29(3):43 − 46. [JIANG Baoliang,FU Beifeng,ZHAO Yantao. Analysis prediction and resources evaluation of spring dynamic:A case study of Bai spring in Hui County[J]. Hydrogeology & Engineering Geology,2002,29(3):43 − 46. (in Chinese with English abstract) doi:  10.3969/j.issn.1000-3665.2002.03.013
    姜宝良, 赵贵章, 于怀昌, 等. 河南新乡百泉供水研究[M]. 北京: 地质出版社, 2012

    JIANG Baoliang, ZHAO Guizhang, YU Huaichang, et al. Study on water supply of Baiquan in Xinxiang, Henan [M]. Beijing: Geological Publishing House, 2012. (in Chinese)
    姜宝良,许来慧,崔江利,等. 新乡市百泉泉水流量动态预测与资源评价[J]. 人民黄河,2013,35(12):71 − 72. [JIANG Baoliang,XU Laihui,CUI Jiangli,et al. Dynamic prediction of spring flow and resources evaluation of Baiquan at Xinxiang[J]. Yellow River,2013,35(12):71 − 72. (in Chinese with English abstract)
    肖荣鸽,靳帅帅,庄琦,等. 基于灰色理论的油气管道腐蚀速率预测[J]. 热加工工艺,2022,51(18):53 − 57. [XIAO Rongge,JIN Shuaishuai,ZHUANG Qi,et al. Prediction of corrosion rate of oil and gas pipeline based on grey theory[J]. Hot Working Technology,2022,51(18):53 − 57. (in Chinese with English abstract)
    曾波, 李树良, 孟伟. 灰色预测理论及其应用[M]. 北京: 科学出版社, 2020

    ZENG Bo, LI Shuliang, MENG Wei. Grey prediction theory and its applications[M]. Beijing: Science Press, 2020. (in Chinese)
    吴磊, 陶忠, 赵志曼, 等. 基于NSGM(1, 3)模型的短切聚丙烯纤维-磷建筑石膏复合材料强度预测[J]. 材料导报, 2021, 35(增刊2): 655 − 659

    WU Lei, TAO Zhong, ZHAO Zhiman, et al. Strength prediction of short-cut PP fiber-phosphorus building gypsum composites based on NSGM(1, 3) model[J]. Materials Reports, 2021, 35(Sup 2): 655 − 659. (in Chinese with English abstract)
    穆奎,马崇启. 梳棉工艺对单纱强力影响的灰色GM(0,N)预测模型[J]. 纺织学报,2011,32(6):34 − 38. [MU Kui,MA Chongqi. Grey GM(0,N) prediction model of influence of carding process on yarn strength[J]. Journal of Textile Research,2011,32(6):34 − 38. (in Chinese with English abstract)
    刘思峰. 灰色系统理论及其应用[M]. 9版. 北京: 科学出版社, 2021.

    LIU Sifeng. The grey system theory and its application[M]. 9th ed. Beijing: Science Press, 2021. (in Chinese)
    姜宝良,陈宁宁,李小建,等. 河南某大型裂隙岩溶水源地地下水位动态分析[J]. 水文地质工程地质,2021,48(2):37 − 43. [JIANG Baoliang,CHEN Ningning,LI Xiaojian,et al. A dynamic analysis of groundwater levels in a large fractured-Karst groundwater wellfield in Henan[J]. Hydrogeology & Engineering Geology,2021,48(2):37 − 43. (in Chinese with English abstract)
    刘德林. 郑州市年降水量的ARIMA模型预测[J]. 水土保持研究,2011,18(6):249 − 251. [LIU Delin. Annual precipitation forecasting of Zhengzhou City based on ARIMA model[J]. Research of Soil and Water Conservation,2011,18(6):249 − 251. (in Chinese with English abstract)
    张浪,贺中华,夏传花,等. 基于河流出水量的区域水文干旱特征及其水文频率分析—以黔中水利枢纽工程区为例[J]. 科学技术与工程,2021,21(27):11480 − 11489. [ZHANG Lang,HE Zhonghua,XIA Chuanhua,et al. Analysis of regional hydrological drought characteristics and hydrological frequency based on river water output:Taking Qianzhong water conservancy project area as an example[J]. Science Technology and Engineering,2021,21(27):11480 − 11489. (in Chinese with English abstract) doi:  10.3969/j.issn.1671-1815.2021.27.005
  • 加载中


    通讯作者: 陈斌,
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(5)

    Article Metrics

    Article views (83) PDF downloads(68) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint