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Full-Text Articles in Data Science
Verifying Empirical Predictive Modeling Of Societal Vulnerability To Hazardous Events: A Monte Carlo Experimental Approach, Yi Victor Wang, Seung Hee Kim, Menas C. Kafatos
Verifying Empirical Predictive Modeling Of Societal Vulnerability To Hazardous Events: A Monte Carlo Experimental Approach, Yi Victor Wang, Seung Hee Kim, Menas C. Kafatos
Institute for ECHO Articles and Research
With the emergence of large amounts of historical records on adverse impacts of hazardous events, empirical predictive modeling has been revived as a foundational paradigm for quantifying disaster vulnerability of societal systems. This paradigm models societal vulnerability to hazardous events as a vulnerability curve indicating an expected loss rate of a societal system with respect to a possible spectrum of intensity measure (IM) of an event. Although the empirical predictive models (EPMs) of societal vulnerability are calibrated on historical data, they should not be experimentally tested with data derived from field experiments on any societal system. Alternatively, in this paper, …
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa
Dissertations, Master's Theses and Master's Reports
An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
A Deep Bilstm Machine Learning Method For Flight Delay Prediction Classification, Desmond B. Bisandu Phd, Irene Moulitsas Phd
Journal of Aviation/Aerospace Education & Research
This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features …