arXiv Machine Learning

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

arXiv:2606. 29117v1 Announce Type: cross Abstract: Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization.

arXiv Machine Learning

Aug 11

Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.

By Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi

fine-tuning

arXiv AI

Jul 14

RepTran: Search-Based Repair of Transformer Models

arXiv:2607. 11193v1 Announce Type: cross Abstract: To ensure the overall quality of AI-enabled software, not only traditional software components but also AI components need to be tested and repaired.

By Yuta Ishimoto, Paolo Arcaini, Fuyuki Ishikawa, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei

llms benchmarks

arXiv Machine Learning

Aug 4

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

arXiv:2608. 01819v1 Announce Type: new Abstract: To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical.

By Fatih \"Urgen, Do\u{g}ay Alt{\i}nel

arXiv Machine Learning

Aug 6

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series

arXiv:2608. 04706v1 Announce Type: new Abstract: Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to make informed decisions in a phase of increasing deployment activities.

By Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer

llms benchmarks

arXiv Machine Learning

Aug 7

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.

By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu

benchmarks safety

arXiv Machine Learning

Jun 19

SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

arXiv:2606. 20451v1 Announce Type: cross Abstract: Competing risks are commonly observed in engineering fields and can bring challenges to time-to-event data modeling when the application scenarios are complicated.

By Jie Min, Yueyao Wang, Mengkun Chen

arXiv AI

Jun 16

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

multimodal

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv AI

Jun 11

LSTM-Based Detection of Structural Breaks in Property Insurance Loss Reserving: A Climate-Informed Approach

arXiv:2606. 11463v1 Announce Type: cross Abstract: Accurate loss reserving is foundational to insurer solvency, yet accelerating climate driven catastrophes systematically violate the stability assumptions on which traditional actuarial methods depend.

By Thomas Mbrice, Shashwat Panigrahi

arXiv AI

Jul 29

Long-Term PM2.5 Forecasting Using a DTW-Enhanced CNN-GRU Model

arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.

By Amirali Ataee Naeini, Arshia Ataee Naeini, Fatemeh Karami Mohammadi, Omid Ghaffarpasand

llms benchmarks

arXiv Machine Learning

Jul 7

Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk

arXiv:2510. 25147v3 Announce Type: replace Abstract: To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas.

By Weimin Huang, Ryan Piansky, Bistra Dilkina, Daniel K. Molzahn

arXiv AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

safety

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