Preprints

Filtering by Subject: Computational Engineering

Mapping Cheatgrass Along California’s Roadways and Powerlines to Identify High-Risk Ignition Zones

Srikantnag Angondalli Nagaraja, Dorottya Fuzy, Istvan Kereszy, et al.

Published: 2025-01-29
Subjects: Climate, Computational Engineering

Between 2001 and 2023, wildfires in the Wildland Urban Interface (WUI) caused by power lines, vehicles, and equipment accounted for approximately 23% of the total area burned by identified ignition sources, burning an estimated 3 million acres in California alone. These ignition sources have been major contributors to the destruction of infrastructure, loss of life, and air pollution in WUI [...]

From Vegetation to Vulnerability: Integrating Remote Sensing and AI to Combat Cheatgrass-Induced Wildfire Hazards in California

Srikantnag Angondalli Nagaraja, Istvan Kereszy, Chang Zhao, et al.

Published: 2024-12-17
Subjects: Computational Engineering, Environmental Indicators and Impact Assessment

Wildfire risk is on the rise around the world. In places like California, this risk is further instigated by the invasive species cheatgrass (Bromus tectorum). Cheatgrass is highly flammable and benefits from wildfires, allowing it to replace native plant communities. Through increasing both the intensity and the frequency of wildfires, it endangers not only its natural environment but also human [...]

COVID-19 Outbreak Prediction with Machine Learning

Sina Faizollahzadeh Ardabili, Amir Mosavi, Pedram Ghamisi, et al.

Published: 2020-10-10
Subjects: Computational Engineering, Diseases, Engineering, Health Information Technology, Medicine and Health Sciences, Other Medicine and Health Sciences, Public Health

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of [...]

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