首页|Findings from Virginia Polytechnic Institute and State University (Virginia Tech) Has Provided New Data on Machine Learning (A Novel Machine Learning and Deep Learning Semi-supervised Approach for Automatic Detection of Insar-based Deformation …)
Findings from Virginia Polytechnic Institute and State University (Virginia Tech) Has Provided New Data on Machine Learning (A Novel Machine Learning and Deep Learning Semi-supervised Approach for Automatic Detection of Insar-based Deformation …)
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Elsevier
New research on Machine Learning is the subject of a report. According to news originating from Blacksburg, Virginia, by NewsRx editors, the research stated, “Over the past two decades, Interferometric synthetic aperture radar (InSAR) has been invaluable for studying earth surface deformation and related effects. Deformation maps generated through multi-temporal InSAR processing methods are however difficult to interpret accurately by general individual users, decision-makers, and non-domain experts owing to the volume, variety, and velocity they are produced.” Financial supporters for this research include National Science Foundation (NSF), United States Geological Survey, United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Virginia Polytechnic Institute and State University (Virginia Tech), “This paper proposes a semi-supervised machine learning based information mining approach to simplify these deformation maps and detect hotspots by extracting prominent signals from time series deformation. The approach initially combines two machine learning based clustering methods named time series k-means (TSKM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to derive clusters with unique spatiotemporal deformation behavior, using time series deformation output generated from Wavelet-based InSAR (WabInSAR) method. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop two deep learning models, one using long short term memory (LSTM) networks alone and another using a combination of LSTM and single-layer perceptron for supervised training. The developed LSTM and LSTM + Perceptron models efficiently learn from the cluster labels, reaching an accuracy of 97.3 %. Further, the deep learning models significantly reduce the computational time from orders of days (-5) to hours (-2) while training and from hours to minutes during prediction. We evaluate the developed approach over Los Angeles, a highly challenging area affected by umpteen deformation events that are challenging to categorize. The outcome of the proposed approach produces hotspots of deforming areas in Los Angeles, providing a generalized and more precise picture of events, much appreciable to non-domain experts."
BlacksburgVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPerceptronVirginia Polytechnic Institute and State University (Virginia Tech)
NETL
NSTL
Elsevier
