李家河水库藻密度分布规律的预测研究  

Research on the Prediction of Algal Density Distribution Patterns in Lijiahe Reservoir

作　　者：杨玮伦 高宇轩 张晓辉 YANG Weilun;GAO Yuxuan;ZHANG Xiaohui(Lijiahe Reservoir Management Co.,Ltd.of Xi’an Water Group Co.,Ltd.,Xi’an 710000,China;School of Computer Science and Engineering,Xi’an University of Technology,Xi’an 710048,China)

机构地区：[1]西安水务(集团)李家河水库管理有限公司,陕西西安710000 [2]西安理工大学计算机科学与工程学院,陕西西安710048

出　　处：《水资源开发与管理》2023年第6期75-80,共6页Water Resources Development and Management

摘　　要：为了解水中藻密度与影响因子的关系,本文对李家河水库2021年16种水质监测指标值进行分析研究,明确了藻密度与叶绿素含量的强相关性,确定了影响藻密度的环境因子主要为总磷、水温、pH值和浊度。分别采用多元线性回归和MLP神经网络构建藻密度与环境因子类水质指标的相关模型。对2022年上半年藻密度分布曲线预测发现,两个模型相结合预测效果比单独模型更为准确。研究结果对中小型水库藻类预测与防治具有一定的推广应用价值。In order to understand the relationship between algal density growth and influencing factors in water,this paper analyzes and researches 16 water quality monitoring indicators in Lijiahe Reservoir in 2021.The strong correlation between algal density and chlorophyll content is identified,and the main environmental factors affecting algal density are determined to be total phosphorus,water temperature,pH value,and turbidity.Multiple linear regression and MLP neural network models are used to construct correlation models between algal density and water quality indicators related to environmental factors.The prediction of algal density distribution curves in the first half of 2022 reveals that the combined use of the two models resulted in more accurate predictions than using each model separately.The research findings have certain practical value for the prediction and control of algae in small and medium-sized reservoirs.

关 键 词：藻密度 环境因子 相关性 多元线性回归模型 MLP神经网络模型 

分 类 号：X524[环境科学与工程—环境工程]