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Modeling and Mapping High Water Table for a Coastal Region in Florida using Lidar DEM Data
Ground Water ( IF 2.0 ) Pub Date : 2020-08-17 , DOI: 10.1111/gwat.13041
Caiyun Zhang , Hongbo Su 1 , Tiantian Li 2 , Weibo Liu 2 , Diana Mitsova 3 , Sudhagar Nagarajan 1 , Ramesh Teegavarapu 1 , Zhixiao Xie 2 , Fred Bloetscher 1 , Yan Yong 1
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Predicting and mapping high water table elevation in coastal landscapes is critical for both science application projects like inundation risk analysis and engineering projects like pond design and maintenance. Previous studies of water table mapping focused on the application of geostatistical methods, which cannot predict values beyond an observation spatial domain or generate an ideal pattern for regions with sparse measurements. In this study, we evaluated the multiple linear regression (MLR) and support vector machine (SVM) techniques for high water table prediction and mapping using fine spatial resolution lidar‐derived Digital Elevation Model (DEM) data, and designed an application protocol of these two techniques for high water table mapping in a coastal landscape where groundwater, tide, and surface water are related. Testing results showed that SVM largely improved the high water table prediction with a mean absolute error (MAE) of 1.22 feet and root mean square error (RMSE) of 2.22 feet compared to the application of the ordinary Kriging method which could not generate a reasonable water table. MLR was also promising with a MAE of around 2 feet and RMSE of around 3 feet. The study suggests that both MLR and SVM are valuable alternatives to estimate high water table elevation in Florida. Fine resolution lidar DEMs are beneficial for high water table prediction and mapping.

中文翻译:

使用Lidar DEM数据对佛罗里达沿海地区的高地下水位进行建模和制图

对于淹没风险分析等科学应用项目和池塘设计与维护等工程项目而言,预测和绘制沿海高地下水位高度都是至关重要的。以前的地下水位图研究集中于地统计学方法的应用,这些方法无法预测超出观测空间范围的值或无法为稀疏测量区域生成理想模式。在这项研究中,我们使用精细的空间分辨率激光雷达衍生的数字高程模型(DEM)数据评估了用于高水位预测和制图的多元线性回归(MLR)和支持向量机(SVM)技术,并设计了这些技术的应用协议在沿海地貌与地下水,潮汐和地表水相关的情况下,有两种用于绘制高地下水位的技术。测试结果表明,与无法产生合理水量的普通克里金法相比,支持向量机极大地改善了高水位预测,平均绝对误差(MAE)为1.22英尺,均方根误差(RMSE)为2.22英尺。桌子。MLR也很有希望,其MAE约为2英尺,RMSE约为3英尺。该研究表明,MLR和SVM都是估算佛罗里达州地下水位较高的有价值的替代方法。高分辨率的激光雷达DEM对于高水位预测和制图很有用。MLR也很有希望,其MAE约为2英尺,RMSE约为3英尺。该研究表明,MLR和SVM都是估算佛罗里达州地下水位较高的有价值的替代方法。高分辨率的激光雷达DEM对于高水位预测和制图很有用。MLR也很有希望,其MAE约为2英尺,RMSE约为3英尺。该研究表明,MLR和SVM都是估算佛罗里达州地下水位较高的有价值的替代方法。高分辨率的激光雷达DEM对于高水位预测和制图很有用。
更新日期:2020-08-17
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