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INTERPOLATING HYDROLOGIC DATA USING LAPLACE FORMULATIONTianle Xu (10802667) 14 May 2021 (has links)
Spatial interpolation techniques play an important
role in hydrology as many point observations need to be interpolated to create
continuous surfaces. Despite the availability of several tools and methods for
interpolating data, <a>not all of them work consistently
for hydrologic applications</a><a>. One of the techniques,
Laplace Equation, which is used in hydrology for creating flownets, has rarely
been used for interpolating hydrology data</a>. The objective of this study is
to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic
data and compare it wih other widely used methods such as the inverse distance
weighting (IDW), natural neighbor, and kriging. Comparison is performed
quantitatively for using root mean square error (RMSE), visually for creating
reasonable surfaces and computationally for ease of operation and speed. Data
related to surface elevation, river bathymetry, precipitation, temperature, and
soil moisture data are used for different areas in the United States. RMSE
results show that LF performs better than IDW and is comparable to other
methods for accuracy. LF is easy to use
as it requires fewer input parameters compared to IDW and Kriging.
Computationally, LF is comparable to other methods in terms of speed when the
datasets are not large. Overall, LF offers an robust alternative to existing
methods for interpolating various hydrology data. Further work is required to
improve its computational efficiency with large data size and find out the
effects of different cell size.
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Visualizing and Modeling Mining-Induced Surface SubsidencePlatt, Marcor Gibbons 13 July 2009 (has links) (PDF)
Ground subsidence due to underground coal mining is a complex, narrowly-understood phenomenon. Due to the complicated physical processes involved and the lack of a complete knowledge of the characteristics of overlying strata, the reliability of current prediction techniques varies widely. Furthermore, the accuracy of any given prediction technique is largely dependent upon the accuracy of field measurements and surveys which provide input data for the technique. A valuable resource available for predicting and modeling subsidence is aerial survey technology. This technology produces yearly datasets with a high density of survey points. The following study introduces a method wherein these survey points are converted into elevation plots and subsidence plots using GIS. This study also presents a method, titled the Type-Xi Integration method (TXI method), which improves upon a previous subsidence prediction technique. This method differs from the previous technique in that it incorporates accurate surface topography and considers irregular mine geometry, as well as seam thickness and overburden variations in its predictions. The TXI method also involves comparing predicted subsidence directly to measured subsidence from subsidence plots. In summary, this study illustrates a method of combining data from aerial survey points and mine geometry with subsidence models in order to improve the accuracy of the models.
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Examination of the Barotropic Behavior of the Princeton Coastal Ocean Model in Lake Erie, Using Water Elevations From Gage Stations and Topex/Poseidon AltimetersVelissariou, Vasilia 30 September 2009 (has links)
No description available.
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