Related Experiment Video
Updated: Nov 22, 2025
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.3K
Quantifying people's experience during flood events with implications for hazard risk communication
Nataliya Tkachenko1,2, Rob Procter2,3, Stephen Jarvis4
1Smith School of Enterprise and the Environment, School of Geography and the Environment, Oxford University Centre for the Environment, University of Oxford, Oxford, United Kingdom.
Plos One
|January 7, 2021
Summary
Semantic drift in social media, detected using deep learning and image analysis, can reveal crowd navigation strategies during floods. This helps differentiate between naive and experienced crowds during severe weather events.
Area of Science:
- Distributional semantics
- Natural language processing
- Computer vision
- Social media analysis
Background:
- Semantic drift describes gradual changes in word meanings and sentiments over time, detectable in large text corpora.
- Previous research identified semantic micro-changes in social media related to natural hazards like floods.
- Semantic drift in social media aids in early flood detection and enhances geo-referenced data for event monitoring.
Purpose of the Study:
- To investigate if images linked to semantically drifted social media tags reflect changes in crowd navigation during floods.
- To determine if deep learning can analyze these image-tag associations to understand crowd behavior.
Main Methods:
- Utilized deep learning models to analyze images associated with 'semantically drifted' social media tags.
- Examined social media data emerging around natural hazard events, specifically floods.
- Applied ontological relationships and corpus analysis techniques from previous work.
Main Results:
- Identified that images linked to semantically drifted tags correlate with altered crowd navigation strategies during floods.
- Demonstrated that alternative social media tags can distinguish between naive and experienced crowds during flood events.
- Showcased the potential of image-tag analysis for understanding crowd responses to varying flood severities.
Conclusions:
- Semantic drift analysis, enhanced by deep learning and image recognition, offers insights into crowd behavior during natural disasters.
- Alternative social media tags provide a nuanced understanding of crowd experience and navigation during floods.
- This approach can improve situational awareness and response strategies for flood events.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
195
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
195
01:29Applications of GIS: Disaster Management and Emergency Response
277
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
277
02:41Responses to Drought and Flooding
11.5K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
11.5K
01:11Hazard Rate
268
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
268
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
497
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
497
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
269
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
269