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首页> 外文会议>Conference on remote sensing and modeling of ecosystems for sustainability >Monitoring the total organic carbon concentrations in a lake with the integrated data fusion and machine-learning (IDFM) technique
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Monitoring the total organic carbon concentrations in a lake with the integrated data fusion and machine-learning (IDFM) technique

机译：用综合数据融合和机器学习（IDFM）技术监测湖泊中的总有机碳浓度

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The concentration of total organic carbon (TOC) in surface waters is subject to seasonal variation, as well as abrupt changes in concentration due to events. In drinking water treatment, TOC is a precursor to disinfection byproducts such as total trihalomethanes (TTHM). With the aid of an early warning system for the detection of TOC concentrations, water treatment operators could make more informed decisions and adjust the treatment processes to minimize the generation of disinfection byproducts. In this paper, a near real-time monitoring system is explored using the Integrated Data Fusion and Machine-learning (IDFM) technique to predict the spatial distribution of TOC in a lake based upon surface reflectance data from satellite imagery. Landsat 5 TM and MODIS Terra satellite imagery can be acquired free of charge, yet MODIS has coarse spatial resolution, while Landsat has a lengthy 16 day revisit time. This difficulty is solved using data fusion algorithms to fuse the fine spatial resolution of Landsat with the daily revisit time of MODIS to generate a synthetic image with both high spatial and temporal resolution. To demonstrate the capabilities of IDFM, this case study uses the fused surface reflectance band data and applied machine-learning techniques to reconstruct the spatiotemporal distribution of TOC in Harsha Lake, which serves as the source water intake for the McEwen Water Treatment Plant in Ohio. The results of this application of IDFM were analyzed using 4 statistical indices, which indicated that the Artificial Neural Network model is capable of reconstructing TOC concentrations throughout the lake.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
机译：在地表水中有机碳总量（TOC）的浓度是受季节变化，以及在浓度由于事件的突然变化。在饮用水处理，TOC是一个前体的消毒副产物如三卤甲烷总量（TTHM）。与早期预警系统的检测TOC含量的帮助下，水处理运营商可以做出更明智的决策和调整治疗方案，以尽可能减少消毒副产物的产生。在本文中，近实时监控系统正在使用的集成数据融合和机器学习（IDFM）技术来预测TOC的在基于从卫星图像表面反射率的数据湖的空间分布的探讨。陆地卫星5 TM和MODIS泰拉卫星图像可以免费获得，但MODIS具有粗糙的空间分辨率，而陆地卫星具有冗长16天访时间。这种困难是使用数据融合算法来融合陆地卫星的精细的空间分辨率与MODIS的每日重访时间，以生成同时具有高空间和时间分辨率的合成图像来解决。为了证明IDFM的功能，这种情况下研究使用熔凝表面反射带的数据和应用的机器学习技术来重建TOC的戒湖，其用作源取水为麦克尤恩水处理厂在俄亥俄州的时空分布。使用4个统计指标，这表明人工神经网络模型能够在整个湖面重建TOC浓度的这种应用IDFM的结果进行了分析。©图片，光学仪器工程师（2012）著作权协会（SPIE）。仅供个人使用的摘要下载。

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