Multispectral remote sensing inversion for city landscape water eutrophication based on Genetic Algorithm-Support Vector Machine

IF 2 Q3 Environmental Science

Water Quality Research Journal of Canada Pub Date : 2014-08-01 DOI:10.2166/WQRJC.2014.040

A. Huo, Jia Zhang, C. Qiao, Chen Li, Juan Xie, Jucui Wang, Xu Zhang

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引用次数: 10

Abstract

Eutrophication has become the primary water quality issue for many urban landscape waters in the world. It is a focus in this paper which analyzes Enhanced Thematic Mapper images and quality observation data for 12 consecutive years in 20 parts of the urban landscape water in Xi9an City, China. A water quality model for urban landscape water based on Support Vector Machine (SVM) was established. Based on in situ monitoring data, the model is compared with water quality retrieving methods of multiple regression and back propagation neural network. Results show that the Genetic Algorithm-SVM (GA-SVM) method has better prediction accuracy than the inversion results of the neural network and the traditional statistical regression method. In short, GA-SVM provides a new method for remote sensing monitoring of urban water eutrophication and has more accurate predictions in inversion results [such as chlorophyll a (Chl-a)] in the Xi9an area. Additionally, remote sensing results highly agreed with in situ monitoring data, indicating that the technology is effective and less costly than in situ monitoring. The technology also can be used to evaluate large lake eutrophication.

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基于遗传算法-支持向量机的城市景观水体富营养化多光谱遥感反演

富营养化已成为世界上许多城市景观水体的首要水质问题。本文对西安市20个城市景观水体连续12年的增强型专题地图图像和高质量观测数据进行了分析。建立了基于支持向量机(SVM)的城市景观水体水质模型。基于现场监测数据，将该模型与多元回归和反向传播神经网络的水质反演方法进行了比较。结果表明，遗传算法-支持向量机(GA-SVM)方法比神经网络和传统统计回归方法的反演结果具有更好的预测精度。总之，GA-SVM为城市水体富营养化遥感监测提供了一种新的方法，在西安地区的反演结果[如叶绿素a (Chl-a)]中有更准确的预测。此外，遥感结果与现场监测数据高度一致，表明该技术比现场监测有效且成本较低。该技术还可用于评价大型湖泊的富营养化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Water Quality Research Journal of Canada WATER RESOURCES-

CiteScore

1.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Water Quality Research Journal publishes peer-reviewed, scholarly articles on the following general subject areas: Impact of current and emerging contaminants on aquatic ecosystems Aquatic ecology (ecohydrology and ecohydraulics, invasive species, biodiversity, and aquatic species at risk) Conservation and protection of aquatic environments Responsible resource development and water quality (mining, forestry, hydropower, oil and gas) Drinking water, wastewater and stormwater treatment technologies and strategies Impacts and solutions of diffuse pollution (urban and agricultural run-off) on water quality Industrial water quality Used water: Reuse and resource recovery Groundwater quality (management, remediation, fracking, legacy contaminants) Assessment of surface and subsurface water quality Regulations, economics, strategies and policies related to water quality Social science issues in relation to water quality Water quality in remote areas Water quality in cold climates The Water Quality Research Journal is a quarterly publication. It is a forum for original research dealing with the aquatic environment, and should report new and significant findings that advance the understanding of the field. Critical review articles are especially encouraged.