Preprints
Filtering by Subject: Computer Sciences
Modelling massive AIS streams with quad trees and Gaussian Mixtures
Published: 2020-06-29
Subjects: Computer Sciences, Other Computer Sciences, Physical Sciences and Mathematics
Pressing issues related to the movement of people and goods can be tackled today thanks to improvements in tracking and communications technology that have made it possible to collect movement data on a big scale. Maritime data from the Automatic Identification System (AIS) is one of the fast growing sources of movement data. Existing approaches for AIS data [...]
Fully Automated Carbonate Petrography Using Deep Convolutional Neural Networks
Published: 2020-06-23
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Paleobiology, Paleontology, Physical Sciences and Mathematics, Sedimentology
Carbonate rocks are important archives of past ocean conditions as well as hosts of economic resources such as hydrocarbons, water, and minerals. Geologists typically perform compositional analysis of grain, matrix, cement and pore types in order to interpret depositional environments, diagenetic modification, and reservoir quality of carbonate strata. Such information can be obtained primarily [...]
An Ethical Decision-Making Framework with Serious Gaming: Smart Water Case Study on Flooding
Published: 2020-06-23
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Civil Engineering, Computer Sciences, Databases and Information Systems, Engineering, Engineering Education, Environmental Engineering, Hydraulic Engineering, Physical Sciences and Mathematics, Theory and Algorithms
Sensors and control technologies are being deployed extensively in both urban water networks and rural river systems, leading to unprecedented ability to sense and control our water environment. Because these sensor networks and control systems allow for higher resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented [...]
Geological Facies Modeling Based on Progressive Growing of Generative Adversarial Networks (GANs)
Published: 2020-06-22
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geology, Geophysics and Seismology, Hydrology, Physical Sciences and Mathematics, Sedimentology, Stratigraphy
Geological facies modeling has long been studied to predict subsurface resources. In recent years, generative adversarial networks (GANs) have been used as a new method for geological facies modeling with surprisingly good results. However, in conventional GANs, all layers are trained concurrently, and the scales of the geological features are not considered. In this study, we propose to train [...]
Spatial Statistics on the Geospatial Web
Published: 2020-06-17
Subjects: Computational Engineering, Computer Sciences, Engineering, Physical Sciences and Mathematics
The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for automatic deployment in server runtime [...]
Large model parameter and structural uncertainties in global projections of urban heat waves
Published: 2020-06-10
Subjects: Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability
Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Reliable projections of these extremes are pressingly needed for local actions in the context of extreme event preparedness and mitigation. Such information, however, is not available because current multi-model projections largely lack a representation of urban areas. Here, we use a newly-developed urban climate emulator [...]
Stress Recovery for the Particle-in-cell Finite Element Method
Published: 2020-05-26
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Mathematics, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Tectonics and Structure
The interelement stress in the Finite Element Method is not continuous in nature, and stress projections from quadrature points to mesh nodes often causes oscillations. The widely used particle-in-cell method cannot avoid this issue and produces worse results when there are mixing materials of large strength (e.g., viscosity in Stokes problems) contrast in one element. The post-processing methods [...]
Hidden Stories: Topic Modeling in Hydrology Literature
Published: 2020-05-25
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Hydrology, Library and Information Science, Physical Sciences and Mathematics, Social and Behavioral Sciences
Hydrologic research generates large volumes of peer-reviewed literature across a number of evolving sub-topics. It’s becoming increasingly difficult for scientists and practitioners to synthesize this full body of literature. This study explores topic modeling as a form of unsupervised learning applied to 42,154 article-abstracts from six high-impact (Impact Factor > 0.9) journals (Water [...]
Automated Seismic Source Characterisation Using Deep Graph Neural Networks
Published: 2020-05-25
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics
Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learning models. In this work, we propose a Graph Neural Network (GNN) approach that [...]
Addressing Model Data Archiving Needs for the Department of Energy’s Environmental Systems Science Community
Published: 2020-05-08
Subjects: Computer Sciences, Environmental Sciences, Environmental Studies, Physical Sciences and Mathematics, Social and Behavioral Sciences
Researchers in the Department of Energy’s ESS program use a variety of models to advance robust, scale-aware predictions of terrestrial and subsurface ecosystems. ESS projects typically conduct field observations and experiments coupled with modeling exercises using a model-experimental (ModEx) approach that enables iterative co-development of experiments and models, and ensures that experimental [...]
Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling
Published: 2020-05-06
Subjects: Atmospheric Sciences, Civil and Environmental Engineering, Computational Engineering, Computer Sciences, Earth Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
This study integrates machine learning and particle-resolved aerosol simulations to develop emulators that predict sub-micron aerosol mixing state indices from the Earth System Model (ESM) simulations. The emulators predict aerosol mixing state using only ESM bulk aerosol species concentrations, which do not by themselves carry mixing state information. Here we used PartMC as the [...]
SymAE: an autoencoder with embedded physical symmetries for passive time-lapse monitoring
Published: 2020-04-13
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics
We introduce SymAE, an auto-encoder architecture that learns to separate multichannel passive-seismic datasets into qualitatively interpretable components: one component corresponds to path-specific effects associated with subsurface properties while the other component corresponds to the spectral signature of the passive sources. This information is represented by two latent codes produced by [...]
RainDisaggGAN - Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks
Published: 2020-03-31
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Hydrology, Physical Sciences and Mathematics
Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is an important topic in many geoscientific applications. From a statistical perspective, this presents a high-dimensional and highly under-determined problem. However, recent advances in unsupervised machine learning provide methods for learning such high-dimensional probability [...]
Toward stable, general machine-learned models of the atmospheric chemical system
Published: 2020-03-23
Subjects: Artificial Intelligence and Robotics, Chemistry, Computer Sciences, Earth Sciences, Environmental Chemistry, Environmental Sciences, Physical Sciences and Mathematics
Atmospheric chemistry models—used as components in models that simulate air pollution and climate change—are computationally expensive. Previous studies have shown that machine-learned atmospheric chemical solvers can be orders of magnitude faster than traditional integration methods but tend to suffer from numerical instability. Here, we present a modeling framework that reduces error [...]
A Serious Gaming Framework for Decision Support on Hydrological Hazards
Published: 2020-03-17
Subjects: Civil and Environmental Engineering, Computer Sciences, Databases and Information Systems, Engineering, Environmental Education, Environmental Engineering, Environmental Sciences, Physical Sciences and Mathematics, Software Engineering, Sustainability, Water Resource Management
In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process [...]