基于时间序列模型与BP神经网络的深圳近岸海域富营养化预测
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摘要
根据2013～2017年夏季(8月)深圳近岸海域水质监测数据资料,采用营养质量指数法对深圳近岸海域富营养化状况及其变化趋势进行评价,并建立时间序列预测模型与BP人工神经网络模型分别对该海域2018年无机氮、活性磷酸盐、化学需氧量和叶绿素a含量进行预测,并根据预测结果利用营养质量指数法对2018年深圳海域富营养化水平进行预测,同时根据预测结果对深圳海域营养盐结构进行分析,识别东西部海域的富营养化限制因子,据此提出富营养化水平的改善建议。研究结果表明:2013～2017年深圳近岸海域富营养化水平一致表现为西部海域偏高,东部海域偏低的特征,其中珠江口海域在5年间的平均富营养化水平最高,其余依次为深圳湾>大鹏湾>大亚湾。2018年富营养化水体基本出现在西部海域,东部海域整体富营养化水平较低,以贫营养水平为主。富营养化高风险区主要分布在西部海域,其中茅洲河口与深圳湾海域富营养化风险尤为高,建议在上述区域进行富营养主要因子无机氮和活性磷酸盐的管控,以期降低富营养化发生概率。针对东部海域沙头角湾和坝光海域表现为中营养水平的情况,建议对上述海域进行无机氮和活性磷酸盐的防治,以达到保持海域营养水平现状的目的。
Based on the water quality monitoring data in the summer of Shenzhen coastal water from 2013 to2017,the eutrophication level was evaluated by the method of nutrition quality index and its change trend in coastal waters of Shenzhen was analyzed. At last,the content of inorganic nitrogen,active phosphate,chemical oxygen demand and chlorophyll-a of Shenzhen nearshore area was predicted by time series forecasting model and artificial neural network,then the eutrophication level of 2018 was assessed. In the mean while,the nutrient structure of Shenzhen nearshore area was analyzed to recognize the limiting factor of eutrophication. The results show that,the level of eutrophication in the coastal waters of Shenzhen was unanimously high in west sea area and low in eastern sea area from 2013 to 2017,and the average eutrophication level reached the highest in the Pearl River estuary in5 years,and others successively were Shenzhen bay,Dapeng bay and Daya bay. Eutrophication water basically appeared in the western waters,and the eutrophication level in the eastern waters was relatively low in 2018. The high risk areas of eutrophication are mainly distributed in the western waters,among which,the risk of eutrophication is particularly high in Maozhou estuary and Shenzhen Bay,and suggested that the inorganic nitrogen and active phosphate should be controlled in Maozhou estuary and Shenzhen Bay to reduce the probability of eutrophication. In order to maintain the status of nutrient level in this sea area,the policy of putting prevention should carried out in Shatoujiao bay and Baguang sea area in the eastern sea.
Based on the water quality monitoring data in the summer of Shenzhen coastal water from 2013 to2017,the eutrophication level was evaluated by the method of nutrition quality index and its change trend in coastal waters of Shenzhen was analyzed. At last,the content of inorganic nitrogen,active phosphate,chemical oxygen demand and chlorophyll-a of Shenzhen nearshore area was predicted by time series forecasting model and artificial neural network,then the eutrophication level of 2018 was assessed. In the mean while,the nutrient structure of Shenzhen nearshore area was analyzed to recognize the limiting factor of eutrophication. The results show that,the level of eutrophication in the coastal waters of Shenzhen was unanimously high in west sea area and low in eastern sea area from 2013 to 2017,and the average eutrophication level reached the highest in the Pearl River estuary in5 years,and others successively were Shenzhen bay,Dapeng bay and Daya bay. Eutrophication water basically appeared in the western waters,and the eutrophication level in the eastern waters was relatively low in 2018. The high risk areas of eutrophication are mainly distributed in the western waters,among which,the risk of eutrophication is particularly high in Maozhou estuary and Shenzhen Bay,and suggested that the inorganic nitrogen and active phosphate should be controlled in Maozhou estuary and Shenzhen Bay to reduce the probability of eutrophication. In order to maintain the status of nutrient level in this sea area,the policy of putting prevention should carried out in Shatoujiao bay and Baguang sea area in the eastern sea.
引文
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14 黄杰.苏州河营养元素的时间序列分析和富营养化风险预测[D].上海:东华大学,2006Huang Jie. The time series analysis of nutrient elementa and to forecast risk of eutrophication in Suzhou creek[D]. Shanghai:Donghua University,2006
15 裴洪平,罗妮娜,蒋勇.利用BP神经网络方法预测西湖叶绿素a的浓度[J].生态学报,2004,24(2):246-251Pei Hongping,Luo Nina,Jiang Yong. Application of back propagation neural network for predicting the concentration of chlorophyll-a in West Lake[J]. Acta Ecologica Sinica,2004,24(2):246-251
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17 李广源.基于遗传算法的洞庭湖富营养化指标预测模型[J].水资源保护,2004,20(5):23-25Li Guangyuan. Genetic algorithm-based prediction model for utrophication index of Dongting Lake[J]. Water Resources Protection,2004,20(5):23-25
18 于春艳,李冕,鲍晨光,等.渤海海域富营养化评价及风险预测[J].海洋环境科学,2015,34(4):373-376Yu Chunyan,Li Mian,Bao Chenguang,et al. Evaluation and risk prediction of eutrophication in Bohai Sea[J]. Marine Environmental Science,2015,34(4):373-376
19 罗华军,刘德富,黄应平.基于遗传算法-支持向量机的水库叶绿素a浓度短期预测非线性时序模型[J].水利学报,2009,40(1):46-51Luo Huajun,Liu Defu,Huang Yingping. Genetic algorithm-support vector machine model for short-term prediction of chlorophyll a concentration nonlinear time series[J]. Journal of Hydraulic Engineering,2009,40(1):46-51
20 Recknagel F,Bobbin J,Whigham P,et al. Comparative application of artificial neural network sand genetic algorithms for multivariate time series modelling of algal blooms in freshwater lakes[J]. Journal of Hydroinformatics,2002,4(2):125-133
21 边佳胤,袁林,王琼.洋山深水港海域水质变化趋势分析及富营养化评价[J].海洋通报,2013,2(1):108-112Bian Jiaying,Yuan Lin,Wang Qiong. Analysis on the trend of seawatere quality variation and assessment of eutrophication at Yangshan Deep-water Port[J]. Marine Science Bulletin,2013,2(1):108-112
22 陈于望,王宪,蔡明宏.湄洲湾海域营养状态评价[J].海洋环境科学,1999,18(3):39-42Chen Yuwang,Wang Xian,Cai Minghong. Assessment on nutrient status in Meizhou Bay[J]. Marine Environmental Science,1999,18(3):108-112
23 郭卫东,章小明,杨逸萍,等.中国近岸海域潜在性富营养化程度的评价[J].应用海洋学学报,1998,17(1):64-70Guo Weidong,Zhang Xiaoming,Yang Yiping,et al. Potential eutrophication assessment for Chinese coastal waters[J]. Journal of Applied of Oceanography,1998,17(1):64-70
2 秦铭俐,蔡燕红,王晓波,等.杭州湾水体富营养化评价及分析[J].海洋环境科学,2009,28(增刊1):53-56Qing Mingli,Cai Yanhong,Wang Xiaobo,et al. Analysis and assessment on eutrophication in Hangzhou Bay[J]. Marine Environmental Science,2009,28(S1):53-56
3 徐艳东,魏潇,吴兴伟,等.庙岛群岛南部海域海水营养盐特征和富营养化评价[J].科学技术与工程,2015,15(22):214-219Xu Yandong,Wei Xiao,Wu Xingwei,et al. Characteristics of nutrients and eutrophication assessment in the southern waters of Miaodao archipelago[J]. Science Technology and Engineering,2015,15(22):214-219
4 徐艳东,魏潇,李佳蕙,等. 2013年春夏季莱州湾海水环境要素特征和富营养化评估[J].中国环境监测,2016,32(6):63-69Xu Yandong,Wei Xiao,Li Jiahui,et al. Characteristics of environment parameters and eutrophication assessment of seawater in Laizhou bay in spring and summer,2013[J]. Environmental Monitoring in China,2016,32(6):63-69
5 牛涛,江天久,陈菊芳.深圳近岸水体富营养化状况及成因研究[C]//中国赤潮研究与防治学术研讨会.北京:海洋出版社,2005:135-143Niu Tao,Jiang Tianjiu,Chen Jufang. Study of eutrophicational state and its reason of Shenzhen coastal sea,symposium on red tide research and control in China[C]//Symposium on Red Tide Research and Control in China,Beijing:China Ocean Press,2005:135-143
6 冷科明.深圳海域赤潮研究[M].北京:海洋出版社,2004Leng Keming. Study of red tide in Shenzhen sea area[M]. Beijing:China Ocean Press,2004
7 牛涛,江天久,陈菊芳.深圳市社会经济发展与近岸水体富营养化关系研究(Ⅰ)[J].海洋环境科学,2006,25(1):41-44Niu Tao,Jiang Tianjiu,Chen Jufang. Studies on the relationshiop between socioeconomic development and eutrophication in coastal water in Shenzhen(Ⅰ)[J]. Marine Environmental Science,2006,25(1):41-44
8 张静,张瑜斌,周凯,等.深圳湾海域营养盐的时空分布及潜在性富营养化程度评价[J].生态环境学报,2010,19(2):253-261Zhang Jing,Zhang Yubin,Zhou Kai,et al. Evaluation on temporal and spatial distribution of nutrients and potential eutrophication in Shenzhen Bay[J]. Ecology and Environment,2010,19(2):253-261
9 孙金水,Wai Onyx Wing-Hon,王伟,等.深圳湾海域氮磷营养盐变化及富营养化特征[J].北京大学学报(自然科学版),2010,46(6):960-964Sun Jinshui,Wai Onyx Wing-Hon,Wang Wei,et al. Characteristics of eutrophication and variation trend of nitrogen and phosphorus nutrient in Shenzhen Bay[J]. Acta Scientiarum Naturalium Universitatis Pekinensis,2010,46(6):960-964
10 戴纪翠,高晓薇,倪晋仁,等.深圳近海海域营养现状分析与富营养化水平评价[J].环境科学,2009,30(10):2879-2883Dai Jicui,Gao Xiaowei,Ni Jinren,et al. Status analysis of nutrients and eutrophication assessment in Shenzhen coastal waters. Environment Science[J]. 2009,30(10):2879-2883
11 谢群,施玉珍,张标标,等.珠江口海域春季富营养化现状与影响分析[J].应用海洋学报,2017,36(3):356-364Xie Qun,Shi Yuzhen,Zhang Biaobiao,et al. Eutrophication status and the influence of Pearl River Estuary in spring[J]. Journal of Applied of Oceanography,2017,36(3):356-364
12 张瑜斌,章洁香,张才学,等.赤潮多发区深圳湾叶绿素a的时空分布及其影响因素[J].生态环境学报,2009,18(5):1638-1643Zhang Yubin,Zhang Jiexiang,Zhang Caixue,et al. The spatial and temporal distribution of chlorophyll a and the influencing factors in Shenzhen Bay with frequent occurrence of red tide[J]. Ecology and Environment,2009,18(5):1638-1643
13 黄浙丰.基于时序神经网络的藻类水华预测模型研究[D].杭州:浙江大学,2011Huang Zhefeng. The research of algal bloom predicting model based on time series nuural network[D]. Hangzhou:Zhejiang University,2011
14 黄杰.苏州河营养元素的时间序列分析和富营养化风险预测[D].上海:东华大学,2006Huang Jie. The time series analysis of nutrient elementa and to forecast risk of eutrophication in Suzhou creek[D]. Shanghai:Donghua University,2006
15 裴洪平,罗妮娜,蒋勇.利用BP神经网络方法预测西湖叶绿素a的浓度[J].生态学报,2004,24(2):246-251Pei Hongping,Luo Nina,Jiang Yong. Application of back propagation neural network for predicting the concentration of chlorophyll-a in West Lake[J]. Acta Ecologica Sinica,2004,24(2):246-251
16 苏新红,金丰军,杨奇志,等.基于BP神经网络模型的福建海域赤潮预报方法研究[J].水产学报,2017,41(11):1744-1755Su Xinhong,Jin Fengjun,Yang Qizhi,et al. Red tide forecasting model based on BP neural network in Fujian sea area[J]. Journal of Fisheries of China,2017,41(11):1744-1755
17 李广源.基于遗传算法的洞庭湖富营养化指标预测模型[J].水资源保护,2004,20(5):23-25Li Guangyuan. Genetic algorithm-based prediction model for utrophication index of Dongting Lake[J]. Water Resources Protection,2004,20(5):23-25
18 于春艳,李冕,鲍晨光,等.渤海海域富营养化评价及风险预测[J].海洋环境科学,2015,34(4):373-376Yu Chunyan,Li Mian,Bao Chenguang,et al. Evaluation and risk prediction of eutrophication in Bohai Sea[J]. Marine Environmental Science,2015,34(4):373-376
19 罗华军,刘德富,黄应平.基于遗传算法-支持向量机的水库叶绿素a浓度短期预测非线性时序模型[J].水利学报,2009,40(1):46-51Luo Huajun,Liu Defu,Huang Yingping. Genetic algorithm-support vector machine model for short-term prediction of chlorophyll a concentration nonlinear time series[J]. Journal of Hydraulic Engineering,2009,40(1):46-51
20 Recknagel F,Bobbin J,Whigham P,et al. Comparative application of artificial neural network sand genetic algorithms for multivariate time series modelling of algal blooms in freshwater lakes[J]. Journal of Hydroinformatics,2002,4(2):125-133
21 边佳胤,袁林,王琼.洋山深水港海域水质变化趋势分析及富营养化评价[J].海洋通报,2013,2(1):108-112Bian Jiaying,Yuan Lin,Wang Qiong. Analysis on the trend of seawatere quality variation and assessment of eutrophication at Yangshan Deep-water Port[J]. Marine Science Bulletin,2013,2(1):108-112
22 陈于望,王宪,蔡明宏.湄洲湾海域营养状态评价[J].海洋环境科学,1999,18(3):39-42Chen Yuwang,Wang Xian,Cai Minghong. Assessment on nutrient status in Meizhou Bay[J]. Marine Environmental Science,1999,18(3):108-112
23 郭卫东,章小明,杨逸萍,等.中国近岸海域潜在性富营养化程度的评价[J].应用海洋学学报,1998,17(1):64-70Guo Weidong,Zhang Xiaoming,Yang Yiping,et al. Potential eutrophication assessment for Chinese coastal waters[J]. Journal of Applied of Oceanography,1998,17(1):64-70