Preprints
Filtering by Subject: Statistical Models
Implications of ambiguity in Antarctic ice sheet dynamics for future coastal erosion estimates: a probabilistic assessment
Published: 2018-11-02
Subjects: Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Probability, Statistical Models, Statistics and Probability
Sea-level rise (SLR) can amplify the episodic erosion from storms and drive chronic erosion on sandy shorelines, threatening many coastal communities. One of the major uncertainties in SLR projections is the potential rapid disintegration of large fractions of the Antarctic ice sheet (AIS). Quantifying this uncertainty is essential to support sound risk management of coastal areas, although it is [...]
SSPipeline: A pipeline for estimating and characterizing uncertainty in coastal storm surge levels
Published: 2018-10-22
Subjects: Computer Sciences, Engineering, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability
Effective management of coastal risks demands projections of flood hazards that account for a wide variety of potential sources of uncertainty. Two typical approaches for estimating flood hazards include (1) direct physical process-based modeling of the storms themselves and (2) statistical modeling of the distributions and relevant characteristics of extreme sea level events. Recently, flexible [...]
Uncertainty in sea level rise projections due to the dependence between contributors
Published: 2018-03-08
Subjects: Applied Statistics, Climate, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Probability, Statistical Models, Statistics and Probability
Sea level rises at an accelerating pace threatening coastal communities all over the world. In this context sea level projections are key tools to help risk mitigation and adaptation. Sea level projections are often made using models of the main contributors to sea level rise (e.g. thermal expansion, glaciers, ice sheets...). To obtain the total sea level these contributions are added, therefore [...]
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Published: 2017-12-04
Subjects: Artificial Intelligence and Robotics, Climate, Computer Sciences, Earth Sciences, Hydrology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability
The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to "quantile crossing", where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environmental sciences, this could, for example, lead to [...]
Compositional Signatures in Acoustic Backscatter Over Vegetated and Unvegetated Mixed Sand-Gravel Riverbeds
Published: 2017-11-01
Subjects: Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geomorphology, Hydraulic Engineering, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability
Multibeam acoustic backscatter has considerable utility for remote characterization of spatially heterogeneous bed sediment composition over vegetated and unvegetated riverbeds of mixed sand and gravel. However, the use of high-frequency, decimeter-resolution acoustic backscatter for sediment classification in shallow water is hampered by significant topographic contamination of the signal. In [...]