Results 131 to 140 of about 2,592 (244)

Decoupling Seasonal Seismic Velocity Changes on Slow‐Moving Slopes in Southwest China Using Tree Ensemble Machine Learning

open access: yesGeophysical Research Letters, Volume 53, Issue 9, 16 May 2026.
Abstract Changes in seismic properties can help assess slope damage, which is influenced by external factors such as temperature, relative humidity, rainfall, air pressure, and local seismicity. However, the contributions of these factors, and how the dominant factor modulates transient and seasonal seismic velocity changes (δv/v $\delta v/v$), remain ...
Dekang Li   +7 more
wiley   +1 more source

Asynchronous Landslide Seasonality Across the United States

open access: yesGeophysical Research Letters, Volume 53, Issue 9, 16 May 2026.
Abstract Mid‐range landslide outlooks can facilitate weather‐related landslide preparedness and disaster response planning, but seasonal landslide activity remains poorly quantified at continental scales. Leveraging >55,000 reported landslides from across the United States (U.S.), we used circular statistics to quantify landslide seasonality in 67 ...
L. V. Luna   +3 more
wiley   +1 more source

Sequencing Finance for Climate Resilience: Instruments, Institutions and Hong Kong's Role in Mobilising Private Capital in Southeast Asia

open access: yesBusiness Strategy and the Environment, Volume 35, Issue 4, Page 6089-6110, May 2026.
ABSTRACT This paper investigates innovative financing strategies to mobilise private capital for climate adaptation, emphasising Hong Kong's role in advancing efforts across Southeast Asia. Using expert interviews and case studies, it addresses two key questions: which financial instruments can strengthen public–private collaboration, and what best ...
Laurence L. Delina   +5 more
wiley   +1 more source

Landslide susceptibility mapping using a machine learning approach and different environmental factors in the Western Ghats region (India)

open access: yesEarth Surface Processes and Landforms, Volume 51, Issue 5, May 2026.
Landslide susceptibility mapping in India's Western Ghats using machine learning revealed high‐risk zones driven by deforestation, slope alteration and road proximity. The Random Forest model showed highest accuracy, supporting targeted mitigation, planning and early warning systems.
Manoranjan Mishra   +4 more
wiley   +1 more source

User Acceptance and Perceptions of Earthquake Early Warning Systems as a Function of Information Type: The Case of Postearthquake Nepal

open access: yesEarthquake Spectra, Volume 42, Issue 2, May 2026.
What drives user perceptions and acceptance of earthquake early warning systems (EEWS) as an emerging technology? Do distinct types of transparency into EEWS affect users’ perceptions of the system's usefulness and desirability differently? To address these questions, we focus on Nepal, an earthquake‐prone country with no active public EEWS ...
Shana Scogin   +3 more
wiley   +1 more source

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