Preprints
Filtering by Subject: Computer Sciences
Methods and Test Cases for Linking Physics-Based Earthquake and Tsunami Models
Published: 2019-09-06
Subjects: Computer Sciences, Earth Sciences, Geology, Geophysics and Seismology, Other Computer Sciences, Physical Sciences and Mathematics
Despite the inter-dependence of long term deformation, earthquakes and tsunamis, few modelling approaches bridge these processes. To advance the understanding of tsunami generation and earthquake-tsunami interactions, we present new methods for linking physics-based models of subduction zone geodynamics and seismic cycling, three-dimensional dynamic earthquake rupture, and tsunami generation, [...]
Analog forecasting of extreme-causing weather patterns using deep learning
Published: 2019-07-31
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computational Engineering, Computer Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based [...]
Segmentation of the Main Himalayan Thrust inferred from geodetic observations of interseismic coupling
Published: 2019-07-04
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Geology, Geomorphology, Geophysics and Seismology, Physical Sciences and Mathematics, Probability, Statistics and Probability, Tectonics and Structure
Mapping the distribution of locked segments along subduction megathrusts is essential for improving quantitative assessments of seismic hazard. Previous geodetic studies suggest the Main Himalayan Thrust (MHT) is homogeneously locked (or coupled) along its complete length over a down-dip extent of ~100 km. However, an increasing number of seismological and geophysical observations suggests the [...]
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory [...]
Guerrilla Badges for Reproducible Geospatial Data Science (AGILE 2019 Short Paper)
Published: 2019-06-19
Subjects: Computer Sciences, Earth Sciences, Physical Sciences and Mathematics
The building blocks of research are developing at an unprecedented pace. Data collection, analysis, interpretation, presentation, review, and publication take place completely on computers. The final product often is still a static document with only limited links to the underlying digital material, making transparency and reproducibility a challenge. In this work we apply the mechanism of badges [...]
Geometry and topology of estuary and braided river channel networks automatically extracted from topographic data
Published: 2019-06-18
Subjects: Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics
Automatic and objective extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous [...]
Understanding Low Cloud Mesoscale Morphology with an Information Maximizing Generative Adversarial Network
Published: 2019-06-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Generative adversarial networks (GANs) are a class of machine learning algorithms with two neural networks, one generator and one discriminator, playing adversarial games with each other. Information maximizing GANs (InfoGANs) is a particular GAN type that tries to maximize mutual information between a subset of latent variables and generated samples, thereby establishing a mapping between the [...]
Characterising Land Cover Change in Brunei Darussalam’s Capital District
Published: 2019-06-03
Subjects: Computer Sciences, Earth Sciences, Environmental Indicators and Impact Assessment, Environmental Monitoring, Environmental Sciences, Natural Resources and Conservation, Physical Sciences and Mathematics
In fast-developing regions, like Southeast-Asia, monitoring urban areas presents a challenge given the lack of publicly available data. This is an issue that precludes the nuances of a citys growth and undermines the way land-use is considered with respect to planning. The issue of data availability is very much present in the small nation of Brunei. Little is still known about the spatiotemporal [...]
Investigation of the Likelihood of Green Infrastructure (GI) Enhancement along Linear Waterways or on Derelict Sites (DS) Using Machine Learning.
Published: 2019-05-08
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Environmental Monitoring, Environmental Sciences, Geographic Information Sciences, Geography, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science
Studies evaluating potential of Green Infrastructure (GI) development using traditional Boolean logic-based multi-criteria analysis methods are not capable of predicting future GI development under dynamic urban scape. This study evaluated robust soft-computing-based methods of artificial intelligence (Artificial Neural Network, Adaptive Neuro-Fuzzy Interface-System) and used statistical [...]
What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling
Published: 2019-04-08
Subjects: Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Engineering, Environmental Sciences, Life Sciences, Mathematics, Medicine and Health Sciences, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability
Computer models are essential tools in the earth system sciences. They underpin our search for understanding of earth system functioning and support decision- and policy-making across spatial and temporal scales. To understand the implications of uncertainty and environmental variability on the identification of such earth system models and their predictions, we can rely on increasingly powerful [...]
Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox
Published: 2019-04-05
Subjects: Applied Mathematics, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Engineering, Environmental Sciences, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability
Global Sensitivity Analysis (GSA) is a set of statistical techniques to investigate the effects of the uncertainty in the input factors of a mathematical model on the model’s outputs. The value of GSA for the construction, evaluation, and improvement of earth system models is reviewed in a companion paper by Wagener and Pianosi [n.d.]. The present paper focuses on the implementation of GSA and [...]
Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network
Published: 2019-02-19
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks (CNNs). We apply the CGAN to generating two-dimensional cloud vertical structures that would be observed by the CloudSat satellite-based radar, using only the collocated Moderate-Resolution [...]
Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework
Published: 2019-02-11
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Hydrology, Physical Sciences and Mathematics, Water Resource Management
As the most recent 3 km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3 km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and [...]
Factor Analysis by R Programming to Assess Variability Among Environmental Determinants of the Mariana Trench
Published: 2019-01-28
Subjects: Computer Sciences, Earth Sciences, Education, Engineering, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geographic Information Sciences, Geography, Geology, Geomorphology, Geophysics and Seismology, Graphics and Human Computer Interfaces, Life Sciences, Marine Biology, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Geography, Other Oceanography and Atmospheric Sciences and Meteorology, Physical and Environmental Geography, Physical Sciences and Mathematics, Programming Languages and Compilers, Remote Sensing, Science and Mathematics Education, Sedimentology, Social and Behavioral Sciences, Spatial Science, Tectonics and Structure, Water Resource Management
The aim of this work is to identify main impact factors affecting variations in the geomorphology of the Mariana Trench which is the deepest place of the Earth, located in the west Pacific Ocean: steepness angle and structure of the sediment compression. The Mariana Trench presents a complex ecosystem with highly interconnected factors: geology (sediment thickness and tectonics including four [...]
Google Earth web service as a support for GIS mapping in geospatial research at universities
Published: 2019-01-25
Subjects: Computer Sciences, Earth Sciences, Ecology and Evolutionary Biology, Education, Environmental Education, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geographic Information Sciences, Geography, Life Sciences, Natural Resources Management and Policy, Other Computer Sciences, Other Earth Sciences, Other Environmental Sciences, Other Geography, Physical and Environmental Geography, Physical Sciences and Mathematics, Remote Sensing, Science and Mathematics Education, Social and Behavioral Sciences, Spatial Science, Terrestrial and Aquatic Ecology
The geospatial work has been performed using combination of the Google Earth imagery, Landsat TM images and Erdas Imagine GIS software. The advantage of utilizing Google Earth scenes with Landsat TM satellite imagery, along with GIS techniques and methods, for inventorying land cover types has been demonstrated for landscape studies. Combination of land cover type characteristics and landscape [...]