Preprints
Filtering by Subject: Statistics and Probability
Higher potential compound flood risk in Northern Europe under anthropogenic climate change
Published: 2018-07-18
Subjects: Applied Mathematics, Atmospheric Sciences, Climate, Earth Sciences, Environmental Sciences, Hydrology, Multivariate Analysis, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Oceanography and Atmospheric Sciences and Meteorology, Other Physical Sciences and Mathematics, Physical Sciences and Mathematics, Physics, Statistics and Probability
The published version of this article is available at https://advances.sciencemag.org/content/5/9/eaaw5531. Compound flooding (CF) is an extreme event taking place in low-lying coastal areas as a result of co-occurring high sea level and large amounts of runoff, caused by precipitation. The impact from the two hazards occurring individually can be significantly lower than the result of their [...]
Preparing for the Utilization of Data Science / Data Analytics in Earth Science Research
Published: 2018-06-21
Subjects: Computational Engineering, Computer Sciences, Databases and Information Systems, Education, Engineering, Mathematics, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Science and Mathematics Education, Statistics and Probability
Data analytics is the process of examining large amounts of varying data types to uncover hidden patterns, unknown correlations and other useful information. The Earth Science Information Partners (ESIP) Earth Science Data Analytics (ESDA) Cluster was created to facilitate the co-analysis of Earth science data and information. In addition to pioneering the definition of ESDA, the cluster has [...]
Landscape classification with deep neural networks.
Published: 2018-06-18
Subjects: Computer and Systems Architecture, Computer Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geology, Geomorphology, Other Statistics and Probability, Physical Sciences and Mathematics, Statistics and Probability
The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely [...]
Spatial association between regionalizations using the information-theoretical V-measure
Published: 2018-04-19
Subjects: Categorical Data Analysis, Computer Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Social and Behavioral Sciences, Statistics and Probability, Theory and Algorithms
There is a keen interest in inferring spatial associations between different variables spanning the same study area. We present a method for quantitative assessment of such associations in the case where spatial variables are either in the form of regionalizations or in the form of thematic maps. The proposed index of spatial association – called the V-measure – is adapted from a measure [...]
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 [...]
Bootstrapped high quantile estimation --- An experiment with scarce precipitation data
Published: 2018-02-25
Subjects: Applied Statistics, Physical Sciences and Mathematics, Probability, Statistics and Probability
This paper details team SUTD’s effort when participating in the “Prediction of extremal precipitation” challenge. We propose a framework that combines the generalized Pareto distribution, a bootstrap resampling scheme and inverse distance weights to capture spatial dependence. Our method reduces the quantile loss functions by 55.1% as compared to a naive benchmark, and shows improvement across [...]
Global assessment and mapping of changes in mesoscale landscapes: 1992–2015
Published: 2018-02-15
Subjects: Categorical Data Analysis, Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Longitudinal Data Analysis and Time Series, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Statistics and Probability
Monitoring global land cover changes is important because of concerns about their impact on environment and climate. The release by the European Space Agency (ESA) of a set of worldwide annual land cover maps covering the 1992–2015 period makes possible a quantitative assessment of land change on the global scale. While ESA land cover mapping effort was motivated by the need to better [...]
Towards machine ecoregionalization of Earth’s landmass using pattern segmentation method
Published: 2018-02-15
Subjects: Categorical Data Analysis, Computer Sciences, Earth Sciences, Environmental Sciences, Geographic Information Sciences, Geography, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Statistics and Probability
We present and evaluate a quantitative method for delineation of ecophysigraphic regions throughout the entire terrestrial landmass. The method uses the new pattern-based segmentation technique which attempts to emulate the qualitative, weight-of-evidence approach to a delineation of ecoregions in a computer code. An ecophysiographic region is characterized by homogeneous physiography defined by [...]
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
Published: 2017-12-05
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 [...]
Geostatistical modelling of cyclic and rhythmic facies architectures
Published: 2017-11-07
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Sedimentology, Statistics and Probability
A pluri-Gaussian method is developed for facies variables in three dimensions to model vertical cyclicity, related to facies ordering, and rhythmicity. Cyclicity is generally characterized by shallowing or deepening-upward sequences and rhythmicity by a low range of variability in cycle thicknesses. Both of these aspects are commonly observed in shallow-marine carbonate successions, especially [...]
Compositional Signatures in Acoustic Backscatter Over Vegetated and Unvegetated Mixed Sand-Gravel Riverbeds
Published: 2017-11-02
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 [...]
Distal turbidites reveal a common distribution for large (>0.1 km3) submarine landslide recurrence
Published: 2017-10-26
Subjects: Earth Sciences, Geology, Physical Sciences and Mathematics, Probability, Sedimentology, Statistics and Probability
Submarine landslides can be far larger than those on land, and are one of the most important processes for moving sediment across our planet. Landslides that are fast enough to disintegrate can generate potentially very hazardous tsunamis, and produce long run-out turbidity currents that break strategically important cable networks. It is important to understand their frequency and triggers. We [...]