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dc.contributor.advisorMoon, Vicki G.
dc.contributor.authorSchicker, Renee Deborahen_NZ
dc.date.accessioned2010-08-23T02:26:43Z
dc.date.available2010-08-23T02:26:43Z
dc.date.issued2010en_NZ
dc.identifier.citationSchicker, R. D. (2010). Quantitative landslide susceptibility assessment of the Waikato region using GIS (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/4399en
dc.identifier.urihttps://hdl.handle.net/10289/4399
dc.description.abstractAreas that have experienced landslide events in the past and the conditioning factors present at these sites can be used to identify areas of the same or similar susceptibility. This can be achieved through a landslide susceptibility assessment using a landslide inventory, a set of predictor variables and specialised computer software. A quantitative landslide susceptibility assessment was conducted for the Waikato Region using two statistical approaches and eleven predictor variables. A landslide inventory map was constructed from the GNS QMap landslide spatial data and GeoNet landslide catalogue. Parameter maps for slope, elevation, aspect, lithology, land cover, soil order, mean monthly rainfall, maximum monthly rainfall, distance from roads, distance from faults and distance from rivers, were constructed and compiled into a database with the landslide inventory. The compiled data underwent both bivariate (weights of evidence) and multivariate (logistic regression) statistical analysis, and a landslide susceptibility map was derived for each. In the weights of evidence approach, the presence and absence of each class in relation to landslide occurrence and non-occurrence was individually assessed for each predictive factor. Logistic regression involves fitting a generalised non-linear model to the data based on a binary predictor (presence or absence of a past landslide event). For each method, a landslide susceptibility map was derived, and the model fit assessed using the landslide inventory. Both susceptibility maps underwent an evaluation to determine the better predictive model. An independent landslide data set was compiled from observations made in Google Earth, and used to establish a set of prediction rate curves and cumulative area curves. Both susceptibility maps resulted in very similar prediction rate curves. Weights of evidence gave a better prediction rate in the 10, 20 and 30% most susceptible pixels, but not in the 40% most susceptible pixels. Neither susceptibility map could be justified as being better than the other based on the prediction rate curves alone. The cumulative area curves for each susceptibility map resulted in very different outcomes. Logistic regression gave the best result with a large proportion of the landslide area within a small proportion of the total area in the high susceptibility classes. Weights of evidence had a larger proportion of the landslide area in high susceptibility classes than logistic regression, but this was associated with a large proportion of total area. Based on the evaluation, the susceptibility map derived using logistic regression was determined to be superior.en_NZ
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dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectlandslideen_NZ
dc.subjectsusceptibilityen_NZ
dc.subjectGISen_NZ
dc.subjectweights of evidenceen_NZ
dc.subjectlogistic regressionen_NZ
dc.subjectWaikatoen_NZ
dc.subjectNew Zealanden_NZ
dc.titleQuantitative landslide susceptibility assessment of the Waikato region using GISen_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineEarth Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2010-02-26en_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/uploads/adt-uow20100226.145107
pubs.place-of-publicationHamilton, New Zealanden_NZ


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