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Machine learning models applied to TSS estimation in a reservoir using multispectral sensor onboard to RPA
Ecological Informatics ( IF 5.1 ) Pub Date : 2021-08-26 , DOI: 10.1016/j.ecoinf.2021.101414
Rafael Luís Silva Dias 1 , Demetrius David da Silva 1 , Elpídio Inácio Fernandes-Filho 2 , Cibele Hummel do Amaral 3 , Erli Pinto dos Santos 1 , Juliana Fazolo Marques 1 , Gustavo Vieira Veloso 2
Affiliation  

Water quality monitoring is fundamental for the maintenance and conservation of water resources. However, the conventional monitoring method, which uses point sampling, does not adequately represent the spatial variability of its parameters, besides being costly. As a result, remote sensing techniques with the use of orbital sensors have been applied to map some water quality parameters, mainly the Optically Active Components (OACs), such as the Total Suspended Solids (TSS), Chlorophyll-a (Chl-a), and Colored Dissolved Organic Matter (CDOM). However, the monitoring of small reservoirs with orbital sensors always presents limitations, such as spectral and spatial problems or time resolution issues, combined with the fact that the sensors are subject to atmospheric disturbances and clouds. Seeking to overcome these issues, the present work aimed to develop a system for monitoring the concentration of Total Suspended Solids (TSS) in reservoirs, based on multispectral sensors onboard a Remotely Piloted Aircraft (RPA). A MicaSense RedEdge multispectral sensor was used, which has five bands in the blue, green, red-edge, and infrared (NIR) spectrum bands. Four campaigns were carried out in two water reservoirs within the Atlantic Forest biome at different seasons of the year. The data were submitted to five machine learning algorithms: the Random Forest (RF), Support Vector Machine Radial Sigma (SVM-RS), Enhanced Adaptive Regression Through Hinges (EARTH), Multiple Linear Regression (MLR), and Cubist algorithms. The results demonstrated that the SVM-RS model was the best adapted to the data patterns (r2 = 0.87), while the RF, EARTH, MLR, and Cubist models presented limitations that hinder their use in the estimation of TSS in reservoirs. This study demonstrated that the use of a multispectral sensor onboard an RPA has high accuracy in estimating TSS in small reservoirs and that machine learning algorithms can generalize the TSS data well.



中文翻译:

使用 RPA 机载多光谱传感器应用于油藏 TSS 估计的机器学习模型

水质监测是维护和保护水资源的基础。然而,使用点采样的传统监测方法不能充分代表其参数的空间变异性,而且成本高昂。因此,使用轨道传感器的遥感技术已被用于绘制一些水质参数,主要是光学活性成分 (OAC),例如总悬浮固体 (TSS)、叶绿素-a (Chl-a) , 和有色溶解有机物 (CDOM)。然而,使用轨道传感器监测小型水库总是存在局限性,例如光谱和空间问题或时间分辨率问题,再加上传感器受到大气扰动和云的影响。为了克服这些问题,目前的工作旨在开发一种基于遥控飞机 (RPA) 上的多光谱传感器监测储层中总悬浮固体 (TSS) 浓度的系统。使用了 MicaSense RedEdge 多光谱传感器,它在蓝色、绿色、红色边缘和红外 (NIR) 光谱波段中有五个波段。在一年中的不同季节,在大西洋森林生物群落内的两个水库中进行了四次运动。数据被提交给五种机器学习算法:随机森林 (RF)、支持向量机径向西格玛 (SVM-RS)、通过铰链的增强自适应回归 (EARTH)、多元线性回归 (MLR) 和 Cubist 算法。结果表明,SVM-RS 模型最适合数据模式(r 基于遥控飞机 (RPA) 上的多光谱传感器。使用了 MicaSense RedEdge 多光谱传感器,它在蓝色、绿色、红色边缘和红外 (NIR) 光谱波段中有五个波段。在一年中的不同季节,在大西洋森林生物群落内的两个水库中进行了四次运动。数据被提交给五种机器学习算法:随机森林 (RF)、支持向量机径向西格玛 (SVM-RS)、通过铰链的增强自适应回归 (EARTH)、多元线性回归 (MLR) 和 Cubist 算法。结果表明,SVM-RS 模型最适合数据模式(r 基于遥控飞机 (RPA) 上的多光谱传感器。使用了 MicaSense RedEdge 多光谱传感器,它在蓝色、绿色、红色边缘和红外 (NIR) 光谱波段中有五个波段。在一年中的不同季节,在大西洋森林生物群落内的两个水库中进行了四次运动。数据被提交给五种机器学习算法:随机森林 (RF)、支持向量机径向西格玛 (SVM-RS)、通过铰链的增强自适应回归 (EARTH)、多元线性回归 (MLR) 和 Cubist 算法。结果表明,SVM-RS 模型最适合数据模式(r 在一年中的不同季节,在大西洋森林生物群落内的两个水库中进行了四次运动。数据被提交给五种机器学习算法:随机森林 (RF)、支持向量机径向西格玛 (SVM-RS)、通过铰链的增强自适应回归 (EARTH)、多元线性回归 (MLR) 和 Cubist 算法。结果表明,SVM-RS 模型最适合数据模式(r 在一年中的不同季节,在大西洋森林生物群落内的两个水库中进行了四次运动。数据被提交给五种机器学习算法:随机森林 (RF)、支持向量机径向西格玛 (SVM-RS)、通过铰链的增强自适应回归 (EARTH)、多元线性回归 (MLR) 和 Cubist 算法。结果表明,SVM-RS 模型最适合数据模式(r2  = 0.87),而 RF、EARTH、MLR 和 Cubist 模型存在的局限性阻碍了它们在储层 TSS 估计中的应用。这项研究表明,在 RPA 上使用多光谱传感器在估计小型储层中的 TSS 方面具有很高的准确性,并且机器学习算法可以很好地概括 TSS 数据。

更新日期:2021-08-31
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