Filtering by Subject: Artificial Intelligence and Robotics

Understanding Low Cloud Mesoscale Morphology with an Information Maximizing Generative Adversarial Network

Tianle Yuan

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 [...]

Investigation of the Likelihood of Green Infrastructure (GI) Enhancement along Linear Waterways or on Derelict Sites (DS) Using Machine Learning.

S M Labib

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 [...]

Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network

Jussi Leinonen, Alexandre Guillaume, Tianle Yuan

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

Hanzi Mao, Dhruva Kathuria, Nicholas Duffield, et al.

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 [...]

Calibration of astigmatic particle tracking velocimetry based on generalized Gaussian feature extraction

Simon Franchini, Alexandros Charogiannis, Christos N. Markides, et al.

Published: 2018-11-20
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Engineering, Fluid Dynamics, Hydrology, Life Sciences, Physical Sciences and Mathematics, Physics

Flow and transport in porous media are driven by pore scale processes. Particle tracking in transparent porous media allows for the observation of these processes at the time scale of ms. We demonstrate an application of defocusing particle tracking using brightfield illumination and a CMOS camera sensor. The resulting images have relatively high noise levels. To address this challenge, we [...]

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

CĂ©line Scheidt, Anjali M Fernandes, Chris Paola, et al.

Published: 2018-09-30
Subjects: Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Earth Sciences, Electrical and Computer Engineering, Engineering, Environmental Engineering, Geology, Geomorphology, Other Civil and Environmental Engineering, Other Engineering, Physical Sciences and Mathematics, Sedimentology, Stratigraphy, Theory and Algorithms

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic [...]

Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

Alex J. Cannon

Published: 2017-12-04
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 [...]


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