EVALUATION AND EARLY WARNING OF WATER ENVIRONMENTAL CARRYING CAPACITY IN BAIYANGDIAN BASIN BASED ON TIME-DIFFERENCE CORRELATION ANALYSIS AND FUZZY NEURAL NETWORK

CHEN Wenting; XIA Qing; SU Jing; ZHENG Mingxia; XI Beidou; XIANG Wei

doi:10.13205/j.hjgc.202206033

Volume 40 Issue 6

Sep. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(6): 261-271

CHEN Wenting, XIA Qing, SU Jing, ZHENG Mingxia, XI Beidou, XIANG Wei. EVALUATION AND EARLY WARNING OF WATER ENVIRONMENTAL CARRYING CAPACITY IN BAIYANGDIAN BASIN BASED ON TIME-DIFFERENCE CORRELATION ANALYSIS AND FUZZY NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 261-271. doi: 10.13205/j.hjgc.202206033

Citation:

CHEN Wenting, XIA Qing, SU Jing, ZHENG Mingxia, XI Beidou, XIANG Wei. EVALUATION AND EARLY WARNING OF WATER ENVIRONMENTAL CARRYING CAPACITY IN BAIYANGDIAN BASIN BASED ON TIME-DIFFERENCE CORRELATION ANALYSIS AND FUZZY NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 261-271. doi: 10.13205/j.hjgc.202206033

Citation:

CHEN Wenting, XIA Qing, SU Jing, ZHENG Mingxia, XI Beidou, XIANG Wei. EVALUATION AND EARLY WARNING OF WATER ENVIRONMENTAL CARRYING CAPACITY IN BAIYANGDIAN BASIN BASED ON TIME-DIFFERENCE CORRELATION ANALYSIS AND FUZZY NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(6): 261-271. doi: 10.13205/j.hjgc.202206033

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EVALUATION AND EARLY WARNING OF WATER ENVIRONMENTAL CARRYING CAPACITY IN BAIYANGDIAN BASIN BASED ON TIME-DIFFERENCE CORRELATION ANALYSIS AND FUZZY NEURAL NETWORK

doi: 10.13205/j.hjgc.202206033

CHEN Wenting^1,2,3,
XIA Qing²,
SU Jing²,
ZHENG Mingxia²,
XI Beidou^{1,2
,
,},
XIANG Wei²

1. School of Water Resources and Environment, China University of Geosciences(Beijing), Beijing 100083, China;
2. State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China;
3. Geological Engineering Department, Qinghai University, Xining 810016, China

Received Date: 2021-12-13
Available Online: 2022-09-01
Publish Date: 2022-09-01

Abstract

Abstract

In order to realize the water environmental carrying capacity evaluation and early warning,a monitoring and early warning index system of water environmental carrying capacity in Baiyangdian Basin was constructed by coupling DPSR model and time difference analysis method.In addition,T-S fuzzy neural network model was built by combining neural network and fuzzy mathematics,and the threshold value of monitoring and early warning index was determined by the control graph method,which solved the randomness and fuzziness of the water environment systems.Finally,the effective evaluation and early warning of water environmental carrying capacity in Baiyangdian Basin were realized.The results showed that:1) the water environmental carrying capacity of Baiyangdian Basin was in a weak carrying state from 2012 to 2015,and in a medium carrying state in 2016 and 2017.The status evaluation level changed from level Ⅳ(orange warning light) to Level Ⅲ(yellow warning light);2) under the current development trend,the overall water environmental carrying capacity of Baiyangdian Basin will increase first and then decrease from 2018 to 2035.The overall water environment of Baiyangdian Basin will deteriorate after 2026,the water environmental carrying capacity will gradually change from a medium carrying state (yellow warning light),to a weak carrying state (orange warning light) and even weaker carrying state (red warning light);3) the growth of the regional population and rapid development of Xiong'an New Area in the future will bring huge pressure to the water environment of Baiyangdian Basin.Therefore,the protection of the regional water environment should be strengthened,the refined environmental control schemes based on spatial units should be implemented to promote the green transformation of the regional economy,the overall improvement and healthy development of regional water environment quality,as well improve the level of regional sustainable development.

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References(48)

References

[1]	李清龙,闫新兴.水环境承载力量化方法研究进展与展望[J].地学前缘,2005,12(特刊):43-48.
[2]	张姗姗,张落成,董雅文,等.基于水环境承载力评价的产业选择:以扬州市北部沿湖地区为例[J].生态学报,2017,37(17):5853-5860.
[3]	高洁.西藏自治区水土资源承载力监测预警初步研究[D].兰州:甘肃农业大学,2018.
[4]	潘军峰.流域水环境承载力理论及应用:以永定河上游为例[D].西安:西安理工大学,2005.
[5]	赵卫,刘景双,孔凡娥.水环境承载力研究述评[J].水土保持研究,2007,14(1):47-50.
[6]	赵松源.基于RBF神经网络模型的松辽流域水环境承载力评价研究[D].长春:吉林大学,2018.
[7]	徐志青,刘雪瑜,袁鹏,等.南京市水环境承载力动态变化研究[J].环境科学研究,2019,32(4):557-564.
[8]	高方述.典型湖区水环境承载力与调控方案研究:以洪泽湖西部湖滨为例[D].南京:南京师范大学,2014.
[9]	段红祥.典型小流域水环境承载力与调控对策研究:以雁栖河为例[D].北京:北京林业大学,2015.
[10]	刘丹,王烜,曾维华,等.基于ARMA模型的水环境承载力超载预警研究[J].水资源保护,2019,35(1):52-55 ,69.
[11]	黄继鸿,雷战波,凌超.经济预警方法研究综述[J].系统工程,2003,21(2):64-70.
[12]	张思梅,张炳传.模糊神经网络在水环境保护中的应用综述[J].安徽建筑工业学院学报(自然科学版),2007,15(2):41-44.
[13]	张文鸽,李会安,蔡大应.水质评价的人工神经网络方法[J].东北水利水电,2004,22(20):42-45.
[14]	杨先野,付强.模糊神经网络在水文水资源应用中的研究进展[J].黑龙江水专学报,2007,34(1):8-11.
[15]	曹文平,刘喜坤,赵天晴,等.基于压力-状态-响应(PSR)模型的潘安湖湿地水环境健康评价[J].环境工程,2021,39(5):231-245.
[16]	王奎峰,李娜,于学峰,等.基于P-S-R概念模型的生态环境承载力评价指标体系研究:以山东半岛为例[J].环境科学学报,2014,34(8):2133-2139.
[17]	程玲洁.基于PSR的福山镇水环境承载力评价研究[D].武汉:华中科技大学,2016.
[18]	左伟,王桥,王文杰,等.区域生态安全评价指标与标准研究[J].地理学与国土研究,2002,18(1):67-71.
[19]	刘苗苗,赵鑫涯,毕军,等.基于DPSR模型的区域河流健康综合评价指标体系研究[J].环境科学学报,2019,39(10):3542-3550.
[20]	任永泰,李丽.哈尔滨市水资源预警模型研究(Ⅰ):基于时差相关分析法的区域水资源预警指标体系构建[J].东北农业大学学报,2011,42(8):136-141.
[21]	任永泰,卢静,付强.基于评价指数的三江平原水安全系统预警研究[J].人民黄河,2017,39(3):75-80.
[22]	TAKAGI T, SUGENO M. Fuzzy identification of systems and its applications to modeling and control[J].IEEE Transactions on Systems,Man and Cybernetics,1985,15(1):116-132.
[23]	钟艳红,苗东昊,赵明汉,等.基于T-S模糊神经网络的水质评价方法及其在四水流域的应用[J].水利科学与寒区工程,2020,3(1):20-24.
[24]	杨程,郭亚昆,郑兰香,等. T-S模糊神经网络模型训练样本构建及其在鸣翠湖水质评价中的应用[J].水动力研究与进展,2020,35(3):356-365.
[25]	李浩楠,刘勇.模糊神经网络的优化及其应用[J].哈尔滨理工大学学报,2020,25(6):142-148.
[26]	李鹏博,林汉良.基于模糊神经网络的城市排水预测[J].科学技术与工程,2020,20(14):5772-5776.
[27]	莫崇勋,阮俞理,莫桂燕,等.基于T-S模糊神经网络模型的钦州市主要河流水质评价[J].人民珠江,2017,38(8):80-83.
[28]	拓守恒,何红,李鹏飞.基于T-S模糊神经网络模型的汉中段汉江流域水质评价与分析[J].计算机时代,2013(8):46-48,51.
[29]	邱焕耀.模糊控制、神经网络和变结构控制的交叉结合及其应用研究[D].广州:华南理工大学,1999.
[30]	FU H X, ZHAO H. MATLAB neural network application design (MATLAB神经网络应用设计)[M].Beijing:China Machine Press,2010.
[31]	CHENG Q M. Study on fuzzy-neural network controller of T-S model and its application[J].Journal of Circuits and Systems,1999,4(1):74-78.
[32]	白洁,王欢欢,刘世存,等.流域水环境承载力评价:以白洋淀流域为例[J].农业环境科学学报,2020,39(5):1070-1076.
[33]	保定市统计局.保定经济统计年鉴[R].保定:保定市统计局,2013-2019.
[34]	河北省国土资源厅.河北省土地调查统计年鉴[R].石家庄:河北省国土资源厅,2012-2017.
[35]	保定市水利局.保定市水资源公报[R].保定:保定市水利局,2012-2017.
[36]	刘苗苗,赵鑫涯,毕军,等.基于DPSR模型的区域河流健康综合评价指标体系研究[J].环境科学学报,2019,39(10):3542-3550.
[37]	曹若馨,张可欣,曾维华,等.基于BP神经网络的水环境承载力预警研究:以北运河为例[J].环境科学学报,2021,41(5):2005-2017.
[38]	柏继云.黑龙江省大豆生产预测预警研究与实证分析[D].哈尔滨:东北农业大学,2006.
[39]	葛慧玲,焦扬,任永泰.哈尔滨市地下水水位预警模型[J].东北农业大学学报,2011,42(2):77-83.
[40]	任永泰,卢静,付强.基于评价指数的三江平原水安全系统预警研究[J].人民黄河,2017,39(3):75-80.
[41]	保定市生态环境局.保定市环统数据[R].保定:保定市生态环境局,2012-2017.
[42]	河北省生态环境厅.河北省环统数据[R].石家庄:河北省生态环境厅,2018.
[43]	李锋.基于综合模拟法的区域旅游投资预警研究:以河南省为例[J].中国人口·资源与环境,2011,21(5):148-156.
[44]	李娜,范海梅,许鹏,等.BP神经网络模型在象山港水环境承载力研究中的应用[J].上海海洋大学学报,2019,28(1):125-133.
[45]	卢莉莉.模糊神经网络在涑水河水质评价中的应用[J].水利技术监督,2017(1):142-145.
[46]	陈文婷,郑明霞,夏青,等.基于产业细化和多要素约束的白洋淀流域水环境承载力系统动力学模拟与调控[J].长江流域资源与环境,2022,31(2):345-357.
[47]	钟艳红,苗东昊,赵明汉,等.基于T-S模糊神经网络的水质评价方法及其在四水流域的应用[J].水利科学与寒区工程,2020,3(1):20-24.
[48]	杨延梅,向维,苏婧,等.基于BP神经网络的白洋淀水环境承载力研究[J].中国农村水利水电,2021(7):1-7.