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Near real-time change detection tool for photogrammetric flood preparedness.
Environ Monit Assess
January 2025
Technische Hochschule Nürnberg Georg Simon Ohm, Institute of Hydraulic Engineering and Water Resources Management, Nuremberg, Germany.
Through the mobilization of movable objects due to the extreme hydraulic conditions during a flood event, blockages, damage to infrastructure, and endangerment of human lives can occur. To identify potential hazards from aerial imagery and take appropriate precautions, a change detection tool (CDT) was developed and tested using a study area along the Aisch River in Germany. The focus of the CDT development was on near real-time analysis of point cloud data generated by structure from motion from aerial images of temporally separated surveys, enabling rapid and targeted implementation of measures.
View Article and Find Full Text PDFPerennial disaster patterns in Central Europe since 2000 and implications for hospital preparedness planning - a cross-sectional analysis.
Sci Rep
January 2025
Heidelberg University, Medical Faculty Heidelberg, Center for Pediatrics and Adolescent Medicine, Pediatric Neurology and Metabolic Medicine, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany.
The goal of this analysis is to describe seasonal disaster patterns in Central Europe in order to raise awareness and improve hospital disaster planning and resilience, particularly during peak events. Hospitals are essential pillars of a country's critical infrastructure, vital for sustaining healthcare services and supporting public well-being-a key issue of national security. Disaster planning for hospitals is crucial to ensure their functionality under special circumstances.
View Article and Find Full Text PDFTemporal Trends in Treatment Interruption among the Victims of Heavy Rain Disasters in Japan: Findings from Emergency Medical Team Data.
Disaster Med Public Health Prep
January 2025
Department of Public Health and Health Policy, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Objectives: Treatment interruptions in disaster victims are concerning, owing to an increase in natural disasters and the growing elderly population with chronic conditions. This study examined the temporal trends in treatment interruptions among victims of 2 recent major heavy rain disasters in Japan: West Japan heavy rain in 2018 and Kumamoto heavy rain in 2020.
Methods: Data for this study were derived from the national standardized medical data collection system called the "Japan Surveillance in Post-Extreme Emergencies and Disasters.
Recommendations for protecting against epidemic diseases in Morocco following recent flooding.
Parasitol Int
December 2024
High Institute of Nursing Professions and Technical Health of Agadir, Ministry of Health and Social Protection, Agadir, Morocco; Ibn Zohr University, Agadir, Morocco. Electronic address:
The recent floods in Morocco, occurring from September 16 to September 30, 2024, have created significant public health challenges, particularly in the regions of Tata, Ouarzazate, and Errachidia. These flooding events have heightened the risk of epidemic diseases, including vector-borne diseases like malaria and leishmaniasis, as well as waterborne infections. This letter outlines critical recommendations to mitigate these risks, including enhanced epidemiological surveillance, distribution of insecticidetreated nets, targeted vector control programs, and community engagement.
View Article and Find Full Text PDFDevelopment of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting.
Sci Rep
December 2024
Department of Civil, Construction and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND, 58108-6050, USA.
A precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons, and the analysis of high-dimensional time series. This research aims to address these specific drawbacks by developing a novel framework for streamflow forecasting.
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