基于PCA-MIC-LSTM的碟形湖溶解氧含量预测模型研究  

Research on dissolved oxygen content prediction model for dish-shaped lake based on PCA-MIC-LSTM

作　　者：迟殿委 黄琪[2] 刘丽贞[3] 方朝阳[2] CHI Dianwei;HUANG Qi;LIU Lizhen;FANG Chaoyang(Artificial Intelligence College,Yantai Institute of Technology,Yantai 264005,China;Key Laboratory of Poyang Lake Wetland and Watershed Research,Jiangxi Normal University,Nanchang 330022,China;Jiangxi Academy of Sciences,Nanchang 330096,China)

机构地区：[1]烟台理工学院人工智能学院,山东烟台264005 [2]江西师范大学鄱阳湖湿地与流域研究教育部重点实验室,江西南昌330022 [3]江西省科学院,江西南昌330096

出　　处：《人民长江》2022年第6期54-60,共7页Yangtze River

基　　金：江西省重点研发计划资助项目(20192ACB70014);江西省青年重点基金资助项目(20192ACBL21022);鄱阳湖湿地与流域研究教育部重点实验室开放基金资助项目(PK2019006)。

摘　　要：溶解氧浓度是湖泊生态健康评价中的关键指标,因浅水碟形湖的水文独特性,使得溶解氧(DO)愈加具有不稳定性和非线性特征。为准确预测碟形湖中的DO浓度,基于鄱阳湖典型碟形湖监测数据集,结合主成分分析法(PCA)、最大信息系数(MIC)和长短时记忆神经网络(LSTM)预测碟形湖DO含量的模型。结果表明:与支持向量回归(SVR)、LSTM模型相比,基于PCA-MIC-LSTM的模型预测精度显著提高,其确定系数高达0.99以上,均方根误差为0.039 mg/L,平均绝对百分误差为0.301%;其中,PCA降噪处理比MIC特征提取更能影响LSTM模型预测的效果,可以显著降低误差率。研究的PCA-MIC-LSTM模型可为同类型湖泊水体保护工作的开展提供参考。Dissolved oxygen concentration is a key indicator in the evaluation of lake ecological health.Due to the unique hydrology of shallow dish-shaped lakes, its dissolved oxygen(DO) has obvious instability and nonlinear characteristics.To accurately predict the DO concentration in the dish-shaped lake, we proposed a prediction model for DO concentrations combining long and short-term memory neural network(LSTM) with principal component analysis(PCA) and maximum information coefficient(MIC) based on the typical monitoring data set of the dish-shaped lake in Poyang Lake.The results showed that compared with the support vector regression(SVR) and LSTM models, the prediction accuracy of the PCA-MIC-LSTM model was significantly improved, with a determination coefficient of over 0.99,a root mean square error of 0.039 mg/L,and an average absolute error rate of 0.301%.Among them, the PCA noise reduction treatment affected the LSTM model prediction effect more than the MIC feature extraction, and can significantly reduce the error rate.The PCA-MIC-LSTM model in this study can provide a reference for the protection of water body in dish-shaped lakes.

关 键 词：溶解氧预测 PCA MIC LSTM 碟形湖 鄱阳湖 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构] X832[自动化与计算机技术—计算机科学与技术]