arXiv Machine Learning August 11, 2026 By Swastik Agrawal, Nishkal Hundia, Ziyue Liu, Michelle Bensi

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

fine-tuning

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 12

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.

By Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar

llms robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 12

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.

By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson

arXiv AI

Jun 17

Remote sensing data imputation using deep learning for multispectral imagery

arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.

By Shuang Liu, Fiona Johnson, Rohitash Chandra

arXiv Machine Learning

Jun 9

Observation-driven correction of numerical weather prediction for marine winds

arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.

By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang

llms rag

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

Related stories

Physics-Informed Machine Learning for Short-Term Flood Prediction

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

Remote sensing data imputation using deep learning for multispectral imagery

Observation-driven correction of numerical weather prediction for marine winds

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