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Using network science to evaluate landslide hazards on Big Sur Coast, California, USA
Abstract. Landslides, ranging from slips to catastrophic failures, pose significant challenges for prediction. This study employs a physically inspired framework to assess landslide hazard at a regional scale (Big Sur Coast, California). Our approach integrates techniques from the study of complex systems with multivariate statistical analysis to identify areas prone to landslide hazards. We successfully apply a technique originally developed on the 2017 Mud Creek landslide and refine our statistical metrics to characterize landslide hazard within a larger geographical area. Our method is compared against factors such as landslide location, slope, displacement, precipitation, and InSAR coherence using multivariate statistical analysis. Our network analyses, which incorporates spatiotemporal dynamics, perform better as a monitoring technique than traditional methods. This approach has potential for real-time monitoring and evaluating landslide hazard across multiple sites.
Holiday gatherings can spread germs. Here are a few tips to stay healthy
The holidays bring family and fun — but also a higher risk of illness. A pediatrician shares simple tips to stay healthy, from washing hands to getting your flu shot.
Cyber Monday sees multiple Apple Watch deals drop to lowest-ever prices
Apple's full lineup of new smartwatches is on sale right now. The best Cyber Monday Apple Watch deals include discounts of up to $200.
North Tyneside GP says debt stress causing mental health issues
GP Dr Kamlesh Sreekissoon says his patients are working multiple jobs to make ends meet.