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Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio

Brown, Michael Kenneth

Abstract Details

2012, Master of Science (MS), Bowling Green State University, Geology.
The purpose of this study was to detect shallow landslides using hillshade maps derived from Light Detection and Ranging (LiDAR)-based Digital Elevation Model (DEM) and validated by field inventory. The landslide susceptibility mapping used an Artificial Neural Network (ANN) approach and back propagation method that was tested in the northern portion of the Cuyahoga Valley National Park CVNP) located in Northeast Ohio. The relationship between landslides and different predictor attributes extracted from the LiDAR-based-DEM such as slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index was evaluated using a Geographic Information System (GIS) based investigation. The approach presented in this thesis required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42 % of known landslides that were associated with 1.6% of total area were correctly predicted. On the other hand, the very low susceptibility class that represented 82 % of the total area was associated with 1 % of correctly predicted landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, which is consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization which is intended to help park officials in effective management and planning.
Peter Gorsevski, PhD (Advisor)
Charles Onasch, PhD (Committee Member)
Xinyue Ye, PhD (Committee Member)
66 p.

Recommended Citations

Citations

  • Brown, M. K. (2012). Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio [Master's thesis, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1350307168

    APA Style (7th edition)

  • Brown, Michael. Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio. 2012. Bowling Green State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1350307168.

    MLA Style (8th edition)

  • Brown, Michael. "Landslide Detection and Susceptibility Mapping Using LiDAR and Artificial Neural Network Modeling: A Case Study in Glacially Dominated Cuyahoga River Valley, Ohio." Master's thesis, Bowling Green State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1350307168

    Chicago Manual of Style (17th edition)