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Developing Tools for Earthquake-induced Landslide Hazard Maps of the Island of Hawaiʻi
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|Title:||Developing Tools for Earthquake-induced Landslide Hazard Maps of the Island of Hawaiʻi|
|Authors:||Namekar, Shailesh Arun|
|Keywords:||earthquake-induced landslide hazard maps|
|Issue Date:||Dec 2013|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [December 2013]|
|Abstract:||The purpose of this study is to develop earthquake-induced landslide hazard maps for the Island of Hawaiʻi using various tools such as Geographical Information Systems (GIS), Artificial Intelligence (AI), and Logistic Regression (LR). The methodology for the current research consists of developing empirical models based on factors considered to be most influential on landslide susceptibility, and analytical models based on conventional slope stability analysis. Earthquake-induced landslide hazard maps were then developed using these models for the Island of Hawaiʻi.|
Empirical models involve systematically studying the landslide contributing factors for the entire island without using any slope stability model. This can be applied to the entire island with generally available island-wide spatial data. Empirical models involve the use of various techniques such as weight analysis, logistic regression and AI based tool such as ANFIS (Adaptive Neuro Fuzzy Inference System). In the weight analysis, different landslide contributing factors were grouped according to their relative importance. These weights were obtained and refined by an Artificial Neural Network (ANN). The map of the Island of Hawaiʻi was divided into cells of size 100m * 100m. Each landslide contributing factor was classified into a common evaluation scale of 1 to 10. Then, landslide contributing factors were multiplied by their respective weights. Finally, the results of the calculations (i.e. final rating values) were obtained. These final rating values were plotted with a color scheme in a map with ten zones of landslide vulnerability (viz., Red (High) (10) to Green (Low) (1). This map showed that weight analysis can locally yield "false positive results. Therefore, two empirical models were developed based on ANFIS and Logistic Regression. In LR-and ANFIS-based models, probability of failure was predicted using landslide contributing factors as inputs. Hazard status values, 0 (Low hazard, i.e. low probability of failure) to 1(High Hazard, i.e. high probability of failure), were derived. Based on these hazard status values, the potential hazard maps were developed. These potential hazard maps provided an overview of potential landslide hazard zones on the Island of Hawaiʻi where more detailed study may be warranted. Though empirical models developed here do not consider seismic analysis, they give a bigger picture of landslide hazard zoning (susceptibility) for the entire island. The maps developed using empirical models can help in selecting regions where further study on slope stability is necessary. For these selected regions, slope stability analysis was carried out with new empirical models developed using explanatory variables from an analytical slope-stability model.
In latter models, the general approach to the landslide zoning method is based on conventional slope stability analyses to determine the Factor of Safety (FS) and critical ground (yield) acceleration (ay) of the individual slopes based on profiles and specific geotechnical and seismic information. Due to inadequate peak shear strength data for the studied regions, along with other considerations, a residual friction angle prediction model using ANFIS was developed based upon available data pairs of residual friction angle and available soil parameters for various locations around the world, including some in Hawaiʻi. The (or These) derived residual friction angle data were then used in slope stability analyses. The Peak Ground Acceleration (PGA) is the most commonly used ground motion parameter in seismic hazard assessment. PGA data at 2% exceedance probabilities in 50 years for the Island of Hawaiʻi (published by USGS) were used for the analyses. Here, probability of exceedance may be defined as probability that an event of specified magnitude will be equaled or exceeded in any defined period of time. Based upon the calculated difference between PGA and ay (yield acceleration), a hazard rating scale was developed. Using this scale, Earthquake-induced landslide hazard zoning maps with ten zones viz., high (1) to low (0), were developed.
For the Island of Hawaiʻi, a new PGA prediction model was developed using ANFIS. This model can reconstruct the PGA for any historical earthquake or future hypothetical earthquake. Hence, PGA values for various historical earthquakes and one hypothetical earthquake were derived using ANFIS. Based upon the calculated difference between these PGA values and ay, earthquake-induced landslide hazard zoning maps with ten zones viz., high (1) to low (0), have been developed. These maps provide hazard status values for various geographically distributed earthquake scenarios.
PGA is a model-derived parameter; hence errors in PGA prediction models are likely to be propagated in the calculated difference between PGA and ay. To minimize the propagation of errors in the PGA prediction model, the parameters used to develop PGA, slope and residual friction angle were related to the hazard status (1, High: Slide) and (0, low: No slide).
Two independent seismic slope stability prediction models were developed based on slope, friction angle, earthquake magnitude, hypocentral distance and average shear wave velocity for earth material from the surface to a depth of 30m as inputs to predict the hazard status (1, High to 0, Low) using LR and ANFIS.
The earthquake-induced landslide hazard maps developed using empirical and analytical models, supported by the historical earthquake-induced slide location database, show that North Kohala, Hamakua, and North Hilo have several areas with high hazard ratings. This reflects the abundant steep slopes, high rainfall conditions, weak soils, and weathered rock on these areas.
The Island of Hawaiʻi has experienced post-earthquake soil and/or rock slope movements. The dataset required for rock slope stability analysis is sparse and incomplete for Hawaiʻi. Therefore, this study relied heavily on reviews of the literature. This review indicates that most rock slope failures were influenced by the presence of pyroclastic materials. Where pyroclastic material underlies massive basalt rocks, it tends to ravel downslope in seismic events thereby undermining the massive basalt blocks.
The landslide hazard maps developed here can be utilized to prioritize further investigation and to highlight where further recommendations on mitigation methodologies may be appropriate. After establishing hazard zones on a landslide hazard map, a detailed site-specific study can be done and necessary slide preventive measures can be taken. The earthquake-induced landslide hazard zoning maps may provide valuable information on the slope stability of large areas, and may be of interest for land use, infrastructure planning, engineering, and hazard mitigation design. The methodologies formulated here could be easily transported to other regions where evaluations of preliminary earthquake-induced landslide potential are desired, especially where data are sparse and/or where regional evaluations have yet to be undertaken.
|Description:||Ph.D. University of Hawaii at Manoa 2013.|
Includes bibliographical references.
|Appears in Collections:||Ph.D. - Civil Engineering|
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