Filtering by Subject: Artificial Intelligence and Robotics

Contemporary and historical detection of small lakes using cross-sensor super resolution Landsat imagery

Ethan D Kyzivat, Laurence C Smith

Published: 2022-11-17
Subjects: Artificial Intelligence and Robotics, Environmental Monitoring, Hydrology

Landsat is the longest-running environmental satellite program and has been used for surface water mapping of large water bodies since its launch in 1972. Remote sensing image resolution is increasingly being enhanced through single image super resolution (SR), a machine learning task typically performed by neural networks. Here, we show that a 10x SR model (Enhanced Super Resolution GAN, or [...]

Deep Learning-Based Super-Resolution of Digital Elevation Models in Data Poor Regions.

Ashok Dahal, Bastian Van den Bout, Cees J. van Westen, et al.

Published: 2022-10-29
Subjects: Artificial Intelligence and Robotics, Computer Engineering, Geomorphology

In order to develop reliable models, the geoscientific community requires high-resolution data sets. However, the collection of such data is a persistent challenge due to the limitations of resources. The concept of super-resolution, a method from the field of machine learning, can be used to predict a high-resolution version of a low-resolution dataset to improve usability in geoscientific [...]

Plant Breeding Biomolecular Classification in Quantum Bayesianism (QBism) Physics-Informed Neural Network Architecture

Karriem A.J. Perry, Barbara S Keary

Published: 2022-08-31
Subjects: Artificial Intelligence and Robotics, Climate, Other Statistics and Probability, Plant Sciences, Probability, Quantum Physics, Research Methods in Life Sciences, Soil Science, Statistical, Nonlinear, and Soft Matter Physics, Sustainability, Systems Biology

In this brief communication, biomolecular plant breeding multi-classification inference is discussed when leveraging the advantages of Physics-informed Neural Network (PiNN) architecture. Albeit, the expected utility of Partial Differential Equation (PDE) inspired neural networks resides in its performance under limited data availability; a variety of neural network configurations result from PDE [...]

Autonomous Passage Planning for a Polar Vessel

Jonathan Daniel Smith, Samuel Hall, George Coombs, et al.

Published: 2022-08-31
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Sustainability

We introduce a method for long-distance maritime route planning in polar regions, taking into account complex changing environmental conditions. The method allows the construction of optimised routes, describing the three main stages of the process: discrete modelling of the environmental conditions using a non-uniform mesh, the construction of mesh-optimal paths, and path smoothing. In order to [...]

Köppen meets Neural Network: Revision of the Köppen Climate Classification by Neural Networks

Ji Luo

Published: 2022-08-23
Subjects: Artificial Intelligence and Robotics, Categorical Data Analysis, Climate, Earth Sciences, Ecology and Evolutionary Biology, Environmental Indicators and Impact Assessment, Environmental Monitoring, Multivariate Analysis

Climate change and development of data-oriented methods are appealing for new climate classification schemes. Based on the most widely used Köppen-Geiger scheme, this article proposes a neural network based climate classification method from a data science perspective. In conventional schemes, empirically handcrafted rules are used to divide climate data into climate types, resulting in certain [...]

A Machine Learning Approach to Finding Factors that Lead to Environmental Friendliness

Sucheer Maddury

Published: 2022-08-22
Subjects: Artificial Intelligence and Robotics, Databases and Information Systems, Environmental Sciences, Numerical Analysis and Scientific Computing

To maintain a sustainable society, environmental friendliness is necessary, an effort that all countries must take part in. The effort must be pioneered by developed nations with the resources to enact sustainable policies, reduce emissions and conserve energy, from which developing nations will follow the eroded path. Recognizing the factors that promote environmental friendliness is necessary [...]

Sinh-arcsinh-normal distributions to add uncertainty to neural network regression tasks: applications to tropical cyclone intensity forecasts

Elizabeth A Barnes, Randal J Barnes, Mark DeMaria

Published: 2022-07-15
Subjects: Artificial Intelligence and Robotics, Earth Sciences, Statistical Methodology

A simple method for adding uncertainty to neural network regression tasks in earth science via estimation of a general probability distribution is described. Specifically, we highlight the sinh-arcsinh-normal distributions as particularly well suited for neural network uncertainty estimation. The methodology supports estimation of heteroscedastic, asymmetric uncertainties by a simple modification [...]

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 by helping communities prepare for hazardous climate-related events. 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 [...]

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

Numerous studies have demonstrated the capability of supervised deep learning techniques for predicting geological features of interest from seismic sections, including features that are difficult to identify using traditional interpretation methods. However, successful application of these techniques in practice has been limited by the difficulty of obtaining large training dataset where seismic [...]

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 [...]


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