PREDICTIVE SEISMICALLY-INDUCED LANDSLIDE HAZARD MAPPING IN OREGON USING A MAXIMUM ENTROPY MODEL (MAXENT)

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1 10NCEE Tenth U.S. National Conference on Earthquake Engineering Frontiers of Earthquake Engineering July 21-25, 2014 Anchorage, Alaska PREDICTIVE SEISMICALLY-INDUCED LANDSLIDE HAZARD MAPPING IN OREGON USING A MAXIMUM ENTROPY MODEL (MAXENT) M. S. O Banion 1 and M. J. Olsen 2 ABSTRACT Oregon is plagued by the occurrence of landslides due to the interactions between its geologic and environmental settings. Landslides dot the hillsides across the entire Coast Range in western Oregon. With the imminent threat of a rupture event along the Cascadian Subduction Zone (CSZ), additional landslides will be triggered. As such, it is critical to identify areas of high susceptibility to landslides, with particular attention focused on the few lifeline transportation routes which cut through the Coast Range. Closure or blockage of these corridors due to damages risks isolating coastal communities from the population base in the Willamette Valley if blocked by landslides following a seismic event. To this end, we generated seismically induced landslide hazard maps for western Oregon using a maximum entropy based modeling approach, MaxEnt. This approach, first introduced in 2004, has conventionally been used for species distribution modeling and geospatial analysis. MaxEnt is based on a presence-only statistical methodology and can generate correlations between occurrence points and predictor variables by removing patterns to maximize randomness. Hence, it is ideally suited to analyze the variety of geospatial and geologic variables that contribute to landslide hazards. For this work, the landslide susceptibility model was developed using geo-referenced landslide occurrence data from the Statewide Landslide Information Database for Oregon (SLIDO) and numerous predictor layers comprised of remote sensing data, categorical lithology distribution, and probabilistic seismic ground acceleration predictions. Examples of remote sensing predictor layers include slope and aspect derived from a digital elevation model as well as annual mean precipitation from PRISM Climate Group and Normalized Difference Vegetation Index (NDVI) data. This analysis technique enables improved understanding of the contribution of causative factors and aids in judging the validity and consistency of output models. 1 Graduate Student Researcher, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR Eric HI and Janice Hoffman Faculty Scholar, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR O Banion MS, Olsen MJ. Predictive Seismically-Induced Landslide Hazard Mapping In Oregon Using a Maximum Entropy Model (MaxEnt) Proceedings of the 10 th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Anchorage, AK, 2014.

2 Predictive Seismically-Induced Landslide Hazard Mapping In Oregon Using a Maximum Entropy Model (MaxEnt) M. S. O Banion 1 and M. J. Olsen 2 ABSTRACT Oregon is plagued by the occurrence of landslides due to the interactions between its geologic and environmental settings. Landslides dot the hillsides across the entire Coast Range in western Oregon. With the imminent threat of a rupture event along the Cascadian Subduction Zone (CSZ), additional landslides will be triggered. As such, it is critical to identify areas of high susceptibility to landslides, with particular attention focused on the few lifeline transportation routes which cut through the Coast Range. Closure or blockage of these corridors due to damages risks isolating coastal communities from the population base in the Willamette Valley if blocked by landslides following a seismic event. To this end, we generated seismically induced landslide hazard maps for western Oregon using a maximum entropy based modeling approach, MaxEnt. This approach, first introduced in 2004, has conventionally been used for species distribution modeling and geospatial analysis. MaxEnt is based on a presenceonly statistical methodology and can generate correlations between occurrence points and predictor variables by removing patterns to maximize randomness. Hence, it is ideally suited to analyze the variety of geospatial and geologic variables that contribute to landslide hazards. For this work, the landslide susceptibility model was developed using geo-referenced landslide occurrence data from the Statewide Landslide Information Database for Oregon (SLIDO) and numerous predictor layers comprised of remote sensing data, categorical lithology distribution, and probabilistic seismic ground acceleration predictions. Examples of remote sensing predictor layers include slope and aspect derived from a digital elevation model as well as annual mean precipitation from PRISM Climate Group and Normalized Difference Vegetation Index (NDVI) data. This analysis technique enables improved understanding of the contribution of causative factors and aids in judging the validity and consistency of output models. Introduction Landslides are prevalent throughout western Oregon due to the interaction between abundant precipitation and the convergent tectonic setting. Coastal communities depend on Highway 101 and the state highways crossing the Coast Range to connect them with the rest of the State of Oregon. Unfortunately, landslides have the potential to isolate these communities from the population base in the Willamette Valley by blocking narrow roadways adjacent to steep slopes. With the imminent threat of one or more large earthquakes striking along the Cascadian Subduction Zone (CSZ), additional landslides will be triggered, adding to the considerable existing landslide hazard. Because the CSZ is capable of generating powerful and long-lasting ground shaking across western Oregon, the geographic scope of potential seismically-induced 1 Graduate Student Researcher, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR Eric HI and Janice Hoffman Faculty Scholar, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR O Banion MS, Olsen MJ. Predictive Seismically-Induced Landslide Hazard Mapping In Oregon Using a Maximum Entropy Model (MaxEnt) Proceedings of the 10 th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Anchorage, AK, 2014.

3 landslides is particularly broad. In addition to damaging and disrupting the state highway system, seismically-induced landslides will obstruct the response to the damage and disruption from other earthquake hazards, such as strong ground shaking, liquefaction, and tsunami waves. We are exploring the development of landslide susceptibility maps for western Oregon (Fig. 1) using a maximum entropy based modeling approach, MaxEnt. This approach, developed by Steven J. Phillips of AT&T Labs along with Miroslav Dudik and Robert E. Schapire of Princeton University, has traditionally been used for species distribution modeling [1]. MaxEnt is based on a presence-only machine learning statistical methodology and can generate correlations between occurrence points and predictor variables by removing patterns to maximize randomness. It is therefore ideally suited to analyze the variety of geospatial and geologic variables that contribute to landslide hazards. MaxEnt has proven to be a very powerful statistical prediction tool and with the exception of very recent work performed by Angel Felicisimo of University Center of Merida, Spain [2] and Matteo Convertino of University of Florida [3], it has not been used for landslide susceptibility modeling. Figure 1. Western Oregon study area. Methods Development of landslide susceptibility hazard maps required review and selection of landslide occurrence points, review and modification of prediction variable layers and refinement of MaxEnt models by parameter adjustment.

4 Landslide Occurrence Data Our primary source of landslide occurrence data for Oregon is the Statewide Landslide Inventory Database of Oregon (SLIDO) compiled by the Oregon Department of Geology and Mineral Industries (DOGAMI). SLIDO includes 10,636 historical landslide point locations as well as 22,542 landslide deposit polygons extracted from 313 published and unpublished studies [4]. Many attributes are associated with the historical landslide point locations, one of which being the approximate date of the slope failure. This tells us that the landslide point features contain sampling bias due to the necessity of their occurrence being observed and reported by a person and, based on the fact that the most recent, documented CSZ earthquake occurred on January 26, 1700, none of the historical landslide point features were directly induced by a seismic event. For the approximately 9,000 SLIDO historical landslide points located within our study area (Fig. 1), the occurrence dates range from 1932 to 2009 and it can be assumed they were predominantly induced by precipitation. In order to further examine the relationship between current ground motion estimates and historical landslides we assimilated the SLIDO landslide deposit polygons into our landslide susceptibility modeling strategy. The SLIDO landslide polygons were compiled based on numerous geologic maps and studies and therefore were not directly observed and assigned an occurrence date. As such, unlike the historical landslide points, there is a potential that some of the landslide polygons were induced by a major CSZ seismic event. Given that occurrence points and not polygons are a required input for running MaxEnt, we converted the landslide polygons to points by randomly assigning 9,000 points within the collection of polygons using ArcGIS. A side by side comparison of the two landslide occurrence data sets is presented in Fig. 2. Figure 2. Comparison of landslide occurrence point datasets.

5 Modification of Peak Ground Acceleration and Velocity Layers The peak ground acceleration (PGA) and peak ground velocity (PGV) data layers used for this analysis originated from the 2012 Oregon Resilience Plan (ORP) for Cascadia Subduction Zone Earthquakes [5]. The ORP PGA and PGV ground motions are based on the USGS synthetic bedrock motions from a M 9.0 Cascadia earthquake. The bedrock ground motions were adjusted for site effects using a newly developed Oregon statewide National Earthquake Hazards Reduction Program (NEHRP) site class map [5]. A detailed review of the ORP PGA and PGV maps revealed the presence of localized regions of amplified ground motion coinciding with mapped landslide deposits. The presence of these regions is attributed to the integration of the NEHRP site class map which uses the statewide geology map and SLIDO landslide polygons to assign appropriate amplification factors. To prevent introducing significant bias to our landslide susceptibility models, we removed areas of amplified ground motion associated with mapped landslide deposits. Using ArcGIS, we began with a polygon representing the entirety of our western Oregon study area and performed an inverse clip with the SLIDO landslide polygon data. The resulting mask, which included all but the landslide polygons was then used to execute the extract by mask tool on the PGA and PGV rasters. All values except those previously associated with a landslide deposit were preserved and landslide zones became null. By way of iterative focal statistics, the null landslide zones were filled in with the mean value surrounding the null region (Fig. 3). Figure 3. Comparison between original ORP PGA layer and the new PGA layer with amplified landslide deposit zones removed. Other Predictor Variables Our first attempts at using MaxEnt for landslide susceptibility modeling utilized numerous

6 predictor layers comprised of remote sensing data, categorical lithology distribution (OGDC v5), and probabilistic seismic PGA and PGV predictions (Oregon Resilience Plan, ORP). The remote sensing predictor layers included slope and aspect derived from a combined LIDAR and National Elevation Dataset 30m digital elevation model (ORP), annual mean precipitation from the PRISM Climate Group (OSU), and Normalized Difference Vegetation Index (NDVI). In addition, we included distance from faults and rivers layers derived from vector data obtained from the Oregon Spatial Data Library. MaxEnt Modeling To further understand how the predictor variables affect the resulting model a jackknife test can be enabled in MaxEnt (Fig. 4). MaxEnt performs a jackknife test by generating numerous models for which each predictor variable is excluded in turn followed by creation of models where each predictor is used in isolation. A comparison between the jackknife models and a model including all predictor variables is then performed to identify the predictor with the highest gain when used in isolation and the predictor that decreases the gain the most when it is omitted. Gain in MaxEnt is closely related to deviance, a measure of goodness of fit used in generalized additive and generalized linear models. At the end of a MaxEnt run, the gain indicates how closely the model is concentrated around the occurrence points [6]. Figure 4. Jackknife results plot for initial MaxEnt model, provided by MaxEnt output. The example jackknife plot presented in Fig. 4, indicates that the Precipitation and Slope predictors provide the most useful information (Fig. 4, see dark blue bar) for development of the model, followed by PGV and then PGA. In addition, because the teal colored bar belonging to the Slope predictor is shortest (farthest from the right) we can conclude that Slope has the largest effect on the model when it is omitted and therefore appears to contain information significantly unique from the other predictors. Based on these results, all subsequent MaxEnt models were generated using the top four predictor variables (Fig. 5).

7 Figure 5. Top four prediction variable layers used for final landslide susceptibility MaxEnt models. In an attempt to distinguish between regions in western Oregon afflicted by seismicallyinduced versus precipitation-induced landslides, we developed four (4) MaxEnt prediction models. Separate seismic and precipitation models were developed for the two landslide occurrence point datasets, the SLIDO historical landslide points, and the points generated from the SLIDO mapped landslide polygons. The seismic models were executed using the PGA, PGV and slope predictor layers and the precipitation models used only the PRISM mean annual precipitation layer and slope. MaxEnt offers numerous options to fine tune a prediction model. One of the more significant global parameters is the regularization multiplier which is used to control the parsimony of the model. The default regularization value is 1.0; anything lower shits towards fitting the model closely to the occurrence points and anything greater will progressively generalize the model and smooth out the response curves. In order to optimize the chosen regularization multiplier for our models we ran numerous iterations of MaxEnt with the aide of BlueSpray, a Java based GIS software developed by SchoonerTurtles, Inc. [7]. BlueSpray contains a module which enables batch processing of numerous MaxEnt models while systematically changing the regularization parameter. In addition, BlueSpray gathers results and performs calculations after each MaxEnt run, including the area under the receiver operating characteristic curve (AUC) and Akaike Information Criterion (AIC), which can be used to evaluate the quality of the model. Because we kept all but the regularization parameter constant, AIC is an ideal way to judge which regularization value results in the best model. AIC is a valuable tool for relative ranking of statistical models; however, all occurrence points and prediction variables associated with the models must be the same in order for comparison of AIC values to be valid. Regularization multiplier values chosen for the 4 MaxEnt models are presented in the following table (Table 1).

8 Table 1. Regularization multiplier values selected for the four MaxEnt models. Model Regularization Multiplier SLIDO Historical Points: Seismic 0.7 SLIDO Historical Points: Precipitation 0.5 SLIDO Landslide Polygon Points: Seismic 0.9 SLIDO Landslide Polygon Points: Precipitation 0.6 Results The thoroughness of the MaxEnt result output provides a surplus of information for aide in judging relative validity among models and understanding the affect predictor variables have on the result. A helpful tool for judging relative validity among models is the area under the receiver operating characteristic curve (AUC) which is provided in the beginning of the MaxEnt output. The AUC compares the likelihood that the probability of landslide occurrence predicted by the model will be higher in a location shared by an actual landslide occurrence than a random location with no landslide occurrence [3]. Ideally, AUC values should be above 0.5, an AUC equal to or below this threshold indicates a random prediction. AUC was originally defined for cases with presences and absences; MaxEnt is a presence-only methodology and therefore does not present a true AUC value [8]. MaxEnt estimates an AUC by using randomly generated background points which are required for development of the prediction results. The issue with using the background point for calculation of AUC is they may or may not represent true absences [8]. For the sake of comparison, BlueSpray s Area Under the Receiver Operator Curve tool was used to generate AUCs which omit the background points and simply look at the relative likelihood of the occurrence points compared to the prediction layer generated by MaxEnt. All AUC values generated by BlueSpray are higher and reveal a different model ranking. It is important to understand the limitations regarding AUC, for instance if what you are trying to predict has a narrow range relative to the study area constrained by the predictor layers, you can generate an erroneously high AUC [6], Table 2 presents the AUC values of the four MaxEnt models. Table 2. Resulting AUC values for the four MaxEnt models. Model MaxEnt AUC BlueSpray AUC SLIDO Historical Points: Seismic SLIDO Historical Points: Precipitation SLIDO Landslide Polygon Points: Seismic SLIDO Landslide Polygon Points: Precipitation

9 The landslide susceptibility maps resulting from the MaxEnt models have been classified to present areas with a high susceptibility to the occurrence of landslides (red) and areas with a low susceptibility (green). The susceptibility zones were classified based on MaxEnt s default prevalence parameter of 0.5. If a grid cell has a relatively high probability of occurrence then MaxEnt assigns a logical value equal to or greater than 0.5. Mapped landslide deposits polygons (black) overlay the results for the purpose of visual validation (Fig. 6). Figure 6. Landslide susceptibility prediction surfaces for precipitation induced (A,B) and seismic induced (C,D) landslides. Beginning with a comparison of the two precipitation-induced landslide susceptibility models (Fig. 6A and 6B), the focused nature of high susceptibility zones in 6A versus the scattered, and seemingly random high zones in Fig. 6B is quite apparent. The SLIDO historical landslide points appear to constrain a superior precipitation induced landslide susceptibility map which correlates well with the assumption that the historical landslide occurrence points were predominantly induced by precipitation. A closer look at map 6A, more specifically regions B2 and B3 show a considerable amount of mapped landslide deposits which fall within areas of supposed low landslide susceptibility. The inability of the precipitation based susceptibility model to predict these zones may indicate that landslides in these areas were triggered by a seismic event. Conversely, a comparison of the seismically-induced landslide maps (Fig. 6C and 6D) identifies 6D, generated using the landslide deposit polygon points, as the superior model. Fig 6C identified hot spots of high susceptibility to seismically-induced landslides; however, they are

10 very localized and demonstrate poor correlation with the mapped landslide point features. This is expected since those landslides from historical records are assumed to be precipitation induced. Fig. 6D performs well with regard to identifying high susceptibility hot spots which correlate well with the larger significant landslide deposit polygons. Combining our presumed best precipitation (blue) and seismically-induced (red) prediction layers (Fig. 7) provides a clear indication of areas potentially susceptible to precipitation and seismically-induced landslides. In areas where the two hazard zones overlap (orange), it is assumed the two triggering mechanisms work together in concert, where precipitation causes additional landslide failures in terrains previously destabilized by a seismic event. Figure 7. Compilation of chosen precipitation induced (blue) and seismic induced (red) landslide susceptibility prediction layers.

11 Conclusions It is likely that the majority of landslides recently inventoried in Oregon were induced by precipitation; however, given the history of significant seismic events it is import to contemplate the role in which seismic events play in the production of landslides. Our results indicate areas throughout western Oregon where precipitation and seismically-induced landslide hazards may stand alone as well as areas where their forces may combine. It is in these combined areas where a cycle of seismically-induced failure followed by precipitation-induced failure of the destabilized material can develop. Predictive landslide models developed using MaxEnt compare well with results obtained using other techniques for this area of Oregon [9]. MaxEnt proved to be very effective at narrowing down prediction variables and we appreciate its surplus of output details. Because there is no available seismically induced landslide database in Oregon, we do not know specifically which landslides or how many of the landslides are actually seismically induced. Further research will test and validate this methodology in an area such as Japan, where seismically induced landslides have been mapped and a database has been developed following a recent earthquake [10]. Acknowledgments This research was funded by the Oregon Department of Transportation and Federal Highway Administration as project SPR-740 and Eric HI and Janice Hoffman. The authors thank Rubini Mahalingam (OSU), Ian Madin (DOGAMI), and Bill Burns (DOGAMI) for providing various data sources and Jim Graham (Humbolt State University) for providing Bluespray. References 1. Phillips SJ, Dudik M, Schapire RE. A maximum entropy approach to species distribution modeling. Proceedings of the Twenty-First International Conference on Machine Learning, 2004; Felicisimo A, Cuartero A, et al. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides, 2011; 10: Convertino M, Troccoli A, Catani F. Detecting fingerprints of landslide drivers: A MaxEnt model. Journal of Geophysical Research: Earth Surface, 2013; 118: Burns WJ, Mickelson KA, Saint-Pierre EC. Statewide Landslide Information Database for Oregon, release 2 (SLIDO-2), Oregon Department of Geology and Mineral Industries, Madin IP, Burns WJ. Ground Motion, Ground Deformation, Tsunami Inundation, Coseismic Subsidence, and Damage Potential Maps for the 2012 Oregon Resilience Plan for Cascadia Subduction Zone Earthquakes. Oregon Department of Geology and Mineral Industries Open-File Report O-13-06, Phillips SJ. A Brief Tutorial on MaxEnt. < BlueSpray GIS Software, 2013, SchoonerTurtles, Inc. < Yackulic CB, Chandler RC, et al. Presence-only modelling using MAXENT: when can we trust the inferences?. Methods in Ecology and Evolution, 2012; 4: Olsen MJ, Ashford SA, et al. Impacts of Potential Seismic Landslides on Lifeline Corridors. Final Report SPR 740, Oregon Department of Transportation, 2013, Under Review. 10. Wartman J, Dunham L, et al. Landslides in Eastern Honshu Induced by the 2011 Tohoku Earthquake. Seismological Society of America, 2012