Science and technology

This theme adresses how science and technology can contribute to DRR, including approaches and initiatives to bring scientific and technical knowledge into practice and policy, approaches for multidisciplinary engagement, good practice in scientific and technical aspects of DRR, and citizen science.

Latest Science & technology additions in the Knowledge Base

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Documents and publications
This report verifies the performance of EFAS service with regard to forecast accuracy; communication clarity and timeliness. In December 2023/January 2024 extensive flooding occurred in Northern Germany in the Ems, Weser and Elbe catchments.
Update
With increased funding, study on cooling the Earth by blocking sunlight is advancing but lacks global oversight. Critics warn of risks like altered rainfall patterns and human rights issues. Inclusive governance is essential for safe progress.
Conversation Media Group, the
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Documents and publications
The report discusses how LiDAR technology improves disaster risk reduction in Banayoyo, Ilocos Sur by enhancing hazard mapping, risk assessments, and planning. It highlights the need for capacity building and integrating LiDAR with other data sources.
Update
Many media articles and broadcasts report that the wet winter of 2023 to 2024 contributed to the magnitude and ferocity of the California fires, but why would a wet winter be a catalyst for spring, summer and autumn wildfires?
Dryad Networks
Update
As interest grows in geoengineering as a strategy for tackling global warming, the world's largest association of Earth and space scientists has launched an ethical framework as a guide to responsible decision-making and inclusive dialogue.
PhysOrg, Omicron Technology Ltd
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Documents and publications
This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy.
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Documents and publications
This study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting.
Research briefs
The finding is an important breakthrough suggesting that a model designed for faults can also be used to predict landslide behavior. The new study used detailed data from two landslide sites in Northern California.
University of California, Santa Cruz
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