其他题名Using Hyperspectral Imagery and GA-PLS Algorithm to Estimate Chemical Oxygen Demand Concentration of Water in River Network
蔡建楠1,2; 刘海龙3; 姜波3; 何甜辉1; 陈文杰2; 冯志伟2; 黎倬琳2; 邢前国3
发表期刊灌溉排水学报
ISSN1672-3317
2020
卷号39期号:9页码:126-131
关键词高光谱遗传算法偏最小二乘法化学需氧量河网水体
研究领域Environmental Sciences & Ecology
英文摘要【Objective】The hyperspectral remote sensing has proven potential to monitor water quality, but issues such as data redundancy and susceptibility to environmental variation could affect its accuracy and reliability. The genetic algorithm-partial least squares (GA-PLS) algorithm with a function to select sensitive spectral variables could resolve these problems. The GA-PLS algorithm was mainly used in retrieval of the optically active parameters such as transparency, chlorophyll-a, suspended matter and turbidity in surface water bodies. The purpose of this paper is to combine it with hyperspectral retrieval model to estimate chemical oxygen demand (COD) concentration of water in the river network in the Pearl River estuary.【Method】Hyperspectral imageries and COD concentration of 146 samples taken from water bodies in the Pearl River estuary were collected, and the characteristic bands of the hyperspectral reflectance data were screened using the GA-PLS algorithm to retrieve the COD concentration. The differences in retrieval accuracy between different band combinations were compared.【Result】The COD concentration retrieved from the hyperspectral imageries based on the GA-PLS algorithm is more accurate than that calculated using the full-spectrum PLS model. The minimum RMSEP of the method was 4.887 mg/L, 11.4% less than that of the full-spectrum PLS model. Using 74 filtered bands, accounting for 2.9% of the full bands, the model was still stable and accurate. Some characteristic bands obtained by the GA-PLS algorithm have physical interpretation, indicating that the screening results were rational.【Conclusion】The GA-PLS algorithm can be used to screen characteristic bands from the hyperspectral imageries to reduce the number of data and simplify the model as a result. It can accurately estimate COD of water in river networks.
中文摘要【目的】建立河网水体化学需氧量(COD)高光谱反演模型,验证遗传-偏最小二乘(GA-PLS)算法对建模效果的改善作用。【方法】采集广东省中山市146个点位的水体高光谱数据和COD质量浓度实测数据,通过GA-PLS算法对高光谱反射率数据进行特征波段筛选后建立COD质量浓度反演模型,并比较输入变量为不同特征波段组合时模型反演效果差异。【结果】基于GA-PLS算法的COD质量浓度高光谱模型反演效果优于全谱段PLS模型,验证集RMSEP最小为4.887 mg/L,较全谱段PLS模型降低11.4%;以筛选得到的74个波段(占全波段数的2.9%)作为输入变量时,模型仍可保持良好的稳定性和反演精度;GA-PLS算法筛选得出的部分特征波段与水体中藻类、悬浮颗粒物的吸收特征波段一致,筛选结果具有合理性和指示意义。【结论】通过GA-PLS算法可对高光谱数据进行特征波段筛选,实现数据降维优化,进一步简化模型;在样本COD质量浓度主要分布范围内,GA-PLS算法模型有良好的反演精度和水质类别分类准确性。该方法在河流COD快速监测中具有良好的应用前景。
文章类型Article
资助机构中国科学院重点仪器项目; 国家自然科学基金项目; 2020年广东省科技创新战略专项
收录类别CSCD
语种中文
CSCD记录号CSCD:6813156
引用统计
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/30334
专题中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
作者单位1.中山市环境监测站,广东中山528403;
2.中山市生态环境局,广东中山528403;
3.中国科学院烟台海岸带研究所/中国科学院海岸带环境过程与生态修复重点实验室,山东烟台264003
推荐引用方式
GB/T 7714蔡建楠,刘海龙,姜波,等. 基于GA-PLS算法的河网水体化学需氧量高光谱反演[J]. 灌溉排水学报,2020,39(9):126-131.
APA蔡建楠.,刘海龙.,姜波.,何甜辉.,陈文杰.,...&邢前国.(2020).基于GA-PLS算法的河网水体化学需氧量高光谱反演.灌溉排水学报,39(9),126-131.
MLA蔡建楠,et al."基于GA-PLS算法的河网水体化学需氧量高光谱反演".灌溉排水学报 39.9(2020):126-131.
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