摘要
针对现有的藻类水华分析存在忽略水体底物中的氮磷反馈机制对藻类生长过程影响以及缺乏对整体水域的全面分析的问题,本研究以富营养化状态和叶绿素a浓度作为藻类水华的表征指标,以遥感图像及水体中的总氮和总磷为主要研究对象,提出一种基于遥感图像3D–CNN及氮磷循环的水华形成过程分析新方法.首先通过3D–CNN对遥感图像进行特征提取,并采用细菌觅食算法优化网络结构,预测水体富营养化等级,在此基础上,根据"氮–磷–藻"之间的耦合关系及底物反馈机制,建立三维生化动力学时变参数模型,确定水华暴发程度及临界条件,并融合遥感图像提取的特征信息建立ENN模型,预测水华暴发的时间及程度.本研究选用由MODIS卫星获取的太湖流域遥感图像及水域中的总氮和总磷等水质监测数据.仿真结果表明,基于遥感图像3D–CNN结合氮磷循环的分析方法在富营养化等级和水华暴发预测方面均取得良好效果.
Most of the existing algal blooms analysis is based on the monitoring data of the water area,which lacks the comprehensive analysis of the whole water area.In this study,the eutrophication state and chlorophyll-a concentration were taken as the characterization indexes of algal blooms,and the remote sensing images and the total nitrogen and phosphorus in the water were taken as the main research data,then a new method based on remote sensing image 3D–CNN and nitrogen and phosphorus cycle is proposed.Firstly,the remote sensing image features are extracted based on 3D–CNN,and the network structure is optimized by bacterial foraging algorithm to predict the water eutrophication level.Secondly,considering the chemical processes such as nitrogen and phosphorus cycle in the process of bloom formation,according to the coupling relationship between‘nitrogen-phosphorus-algae’and substrate feedback mechanism,a three-dimensional biochemical kinetic time-varying parameter model was established to determine the outbreak degree and critical conditions of the bloom.Combined with the characteristic information extracted from remote sensing images,the ENN model was established to predict the time and extent of the bloom.In this study,the remote sensing images of Taihu Lake Basin obtained by MODIS satellite and the water quality monitoring data such as total nitrogen and total phosphorus were selected.The simulation results show that the analysis method based on remote sensing image 3D–CNN combined with nitrogen and phosphorus cycle achieved good results in eutrophication status and water bloom outbreak prediction.
作者
王立
吴羽溪
王小艺
刘载文
WANG Li;WU Yu-xi;WANG Xiao-yi;LIU Zai-wen(Beijing Laboratory For Intelligent Environmental Protection,School of Artificial Intelligence,Beijing Technology&Business University,Beijing 100048,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2021年第10期1683-1691,共9页
Control Theory & Applications
基金
国家自然科学基金项目(61802010)
北京优秀人才培养资助青年拔尖团队项目(2018000026833TD01)
北京市百千万人才工程项目(19BGL184)资助。
关键词
遥感
卷积神经网络CNN
水
水华
预测
remote sensing
convolutional neural networks
water
blooms
prediction
