BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Filters

2020 - 20252

Settings

Author: search.filters.author.Goonetilleke, AshanthaSubject: search.filters.subject.Crowdsourcing

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Determining disaster severity through social media analysis: testing the methodology with South East Queensland Flood tweets
    (Elsevier, 2020-01) Goonetilleke, Ashantha
    Social media was underutilised in disaster management practices, as it was not seen as a real-time ground level information harvesting tool during a disaster. In recent years, with the increasing popularity and use of social media, people have started to express their views, experiences, images, and video evidences through different social media platforms. Consequently, harnessing such crowdsourced information has become an opportunity for authorities to obtain enhanced situation awareness data for efficient disaster management practices. Nonetheless, the current disaster-related Twitter analytics methods are not versatile enough to define disaster impacts levels as interpreted by the local communities. This paper contributes to the existing knowledge by applying and extending a well-established data analysis framework, and identifying highly impacted disaster areas as perceived by the local communities. For this, the study used real-time Twitter data posted during the 2010–2011 South East Queensland Floods. The findings reveal that: (a) Utilising Twitter is a promising approach to reflect citizen knowledge; (b) Tweets could be used to identify the fluctuations of disaster severity over time; (c) The spatial analysis of tweets validates the applicability of geo-located messages to demarcate highly impacted disaster zones.
  • No Thumbnail Available
    Item
    Development of a web application through a mobilized crowdsourcing platform to enable participatory risk sensitive urban development
    (AARS, 2025) Goonetilleke, Ashantha
    Flooding is the most frequent and destructive natural disaster currently facing Sri Lanka. Rapid urbanization and changing precipitation patterns are exacerbating the situation, leading to extensive socio-economic damage and disrupting countless lives. Despite the availability of technology-based applications that can raise disaster awareness and improve management, these tools are not fully utilized in Sri Lankan communities. The study addresses the critical issue of insufficient awareness and the lack of formal early flood alert mechanisms within Sri Lankan. Although, recent technological advancements offer opportunities for community to engage in sharing early disaster warnings among their networks, they remain underutilized. The community engagement in disaster management is still minimal, reducing the preparedness and resilience of vulnerable communities. To address this, a platform integrating a crowdsourcing-based mobile application with a web application was developed, aiming to make disaster management and response inclusive through community involvement and advanced remote sensing technologies. A flood vulnerability assessment model was created using 30 years of historical flood data and nine conditioning factors, including topographic features, weather-related variables, hydrological networks, land cover, and soil type, with Sentinel-2 satellite imagery for the Kelaniya watershed area enhancing the model's accuracy. The mobile application facilitates real-time data collection from individuals in flood-prone areas, who can report on flood levels, affected locations, and other critical information. This crowdsourced data undergoes rigorous verification to ensure accuracy. Once validated, the information is visualized on a web application, serving as a vital communication tool for both the community and disaster response authorities. The methodology includes developing the vulnerability assessment model, creating the mobile application with integrated crowdsourcing techniques, and conducting trial workshops to engage the community and validate the platform with the contribution of relevant authorities. Mobilization strategies are proposed based on insights from these community interactions. By prioritizing community participation and utilizing cutting-edge geo-information technologies, this research significantly contributes to building resilient and proactive urban communities in Sri Lanka. The findings demonstrate the substantial potential of combining crowdsourced data with remote sensing to enhance disaster management and community resilience.