Preprints

Filtering by Subject: Artificial Intelligence and Robotics

An Artificial Neural Network Emulator of the Rangeland Hydrology and Erosion Model

Mahmoud Saeedimoghaddam, Grey Nearing, Mariano Hernandez, et al.

Published: 2022-06-17
Subjects: Artificial Intelligence and Robotics, Dynamic Systems, Hydrology, Natural Resources and Conservation

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Many studies have shown the higher prediction accuracy of the ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computer time of execution. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer [...]

Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

Elizabeth Weirich Benet, Maria Pyrina, Bernat Jiménez Esteve, et al.

Published: 2022-06-02
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences

Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early Warning Systems help to reduce these impacts. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and [...]

Space-time landslide hazard modeling via Ensemble Neural Networks

Ashok Dahal, Hakan Tanyas, Cees J. van Westen, et al.

Published: 2022-06-02
Subjects: Applied Statistics, Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics, Statistics and Probability

For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the [...]

Using Synthetic Data Trained Convolutional Neural Network For Predicting Sub-Resolution Thin Layers From Seismic Data

Dongfang Qu, Klaus Mosegaard, Runhai Feng, et al.

Published: 2022-05-19
Subjects: Artificial Intelligence and Robotics, Geology, Geophysics and Seismology

Complex fault system revealed from 3-D seismic reflection data with deep learning and fault network analysis

Thilo Wrona, Indranil Pan, Rebecca E. Bell, et al.

Published: 2022-05-13
Subjects: Artificial Intelligence and Robotics, Geology, Geophysics and Seismology, Tectonics and Structure

Understanding where normal faults are is critical to an accurate assessment of seismic hazard, the successful exploration for and production of natural (including low-carbon) resources, and for the safe subsurface storage of CO2. Our current knowledge of normal fault systems is largely derived from seismic reflection data imaging intra-continental rifts and continental margins. However, [...]

Automated machine learning to evaluate the information content of tropospheric trace gas columns for fine particle estimates over India: a modeling testbed

Zhonghua Zheng, Arlene M. Fiore, Daniel M. Westervelt, et al.

Published: 2022-03-20
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Environmental Engineering, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology

India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM2.5). Ground-level PM2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using [...]

Camera-Based Intelligent Stream Stage Sensing for Decentralized Environmental Monitoring

Yusuf Sermet, Ibrahim Demir

Published: 2022-03-02
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Electrical and Computer Engineering, Engineering, Environmental Monitoring, Water Resource Management

On average, flood damages cost $4.4 billion in the US annually. Accurate, vast, and real-time coverage of water level monitoring is crucial for the advancement of environmental research, specifically in the areas of climate change, water distribution, and natural disaster preparedness and management. According to a 2018 EPA report, there are 2.7 million streams and associated watersheds in the US [...]

GANSim-3D for conditional geomodelling: theory and field application

Suihong Song, Tapan Mukerji, Jiagen Hou, et al.

Published: 2021-12-22
Subjects: Artificial Intelligence and Robotics, Computational Engineering, Geology, Hydrology, Numerical Analysis and Scientific Computing, Oil, Gas, and Energy, Theory and Algorithms, Water Resource Management

Geomodelling of subsurface reservoirs is important for water resources, hydrocarbon exploitation, and Carbon Capture and Storage (CCS). Traditional geostatistics-based approaches cannot abstract complex geological patterns and are thus not able to simulate very realistic earth models. We present a Generative Adversarial Networks (GANs)-based 3D reservoir simulation framework, GANSim-3D, which can [...]

Bridging knowledge gaps with hybrid machine-learning forest ecosystem models (ML-FEMs): inferential simulation of past understory light regimes

Adam Michael Erickson, Craig Nistchke

Published: 2021-10-29
Subjects: Artificial Intelligence and Robotics, Biodiversity, Biogeochemistry, Computer Sciences, Earth Sciences, Ecology and Evolutionary Biology, Forest Sciences, Life Sciences, Physical Sciences and Mathematics, Plant Sciences

Soil moisture is a key limiting factor of plant productivity in boreal and montane regions, producing additional climate feedbacks through evaporation, regeneration, mortality, and respiration. Understory solar irradiation – the primary driver of surface temperature and evaporative demand – remains poorly represented in vegetation models due to a lack of 3-D canopy geometry. Existing models are [...]

Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

Martijn van den Ende, André Ferrari, Anthony Sladen, et al.

Published: 2021-09-23
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology, Transportation Engineering

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic [...]

Ten Simple Rules for Researchers Who Want to Develop Web Apps

Sheila M. Saia, Natalie G. Nelson, Sierra N. Young, et al.

Published: 2021-07-18
Subjects: Agricultural Science, Agriculture, Applied Statistics, Artificial Intelligence and Robotics, Bioresource and Agricultural Engineering, Computer Sciences, Databases and Information Systems, Environmental Monitoring, Graphics and Human Computer Interfaces, Natural Resources and Conservation, Software Engineering, Terrestrial and Aquatic Ecology, Water Resource Management

Growing interest in data-driven, decision-support tools across the life sciences and physical sciences has motivated development of web applications, also known as web apps. Web apps can help disseminate research findings and present research outputs in ways that are more accessible and meaningful to the general public--from individuals, to governments, to companies. Specifically, web apps enable [...]

Advancing flood warning procedures in ungauged basins with machine learning.

Zimeena Rasheed, Akshay Aravamudan, Ali Gorji Sefidmazgi, et al.

Published: 2021-05-27
Subjects: Artificial Intelligence and Robotics, Hydrology

Flood prediction across scales and more specifically in ungauged areas remains still a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. Building upon the recent success of Machine Learning (ML) models on streamflow prediction, this work presents a prototype ML-based framework for flood warning and flood peak prediction. The fundamental [...]

Data Science for Geoscience: Recent Progress and Future Trends from the Perspective of a Data Life Cycle

Xiaogang Ma

Published: 2021-05-04
Subjects: Artificial Intelligence and Robotics, Databases and Information Systems, Earth Sciences, Environmental Sciences, Numerical Analysis and Scientific Computing, Theory and Algorithms

Data science receives increasing attention in a variety of geoscience disciplines and applications. Many successful data-driven geoscience discoveries have been reported recently, and the number of geoinformatics and data science sessions have begun to increase in many geoscience conferences. Across academia, industry, and governmental sectors, there is a strong interest to know more about the [...]

Knowledge graph construction and application in geosciences: A review

Xiaogang Ma

Published: 2021-04-30
Subjects: Artificial Intelligence and Robotics, Computer Engineering, Computer Sciences, Databases and Information Systems, Earth Sciences, Environmental Sciences

Knowledge graph (KG) is a topic of great interests to geoscientists as it can be deployed throughout the data life cycle in data-intensive geoscience studies. Nevertheless, comparing with the large amounts of publications on machine learning applications in geosciences, summaries and reviews of geoscience KGs are still limited. The aim of this paper is to present a comprehensive review of KG [...]

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data

Martijn van den Ende, Itzhak Lior, Jean Paul Ampuero, et al.

Published: 2021-03-04
Subjects: Artificial Intelligence and Robotics, Geophysics and Seismology

Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterisation, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fibre-optic cables into seismic arrays with a spatial sampling [...]

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