Flood risk prediction has been traditionally based on models that are developed from time-series of data collected over long periods of time from expensive and hard to maintain in situ sensors available only in specific areas. Scent is a EU project which provides an integrated toolbox of smart collaborative and innovating technologies that augment costly in situ infrastructure, enabling citizens to become the ‘eyes’ of the policy makers by monitoring LC/LU changes in their everyday activities and related environmental phenomena like floods by crowdsourcing relevant information. Policy makers and relevant stakeholders are able to set-up citizen science campaigns in areas where specific environmental information is needed through the use of a dedicated tool. These data may include images that are processed through an intelligent engine and classified based on a LC/LU taxonomy, sensor measurements with low-cost portable environmental sensor or river measurements. The citizen-generated data are used to produced LC/LU maps of improved accuracy of the area of interest where taxonomy elements such as river banks are identified and categorized base on their coverage, such as low grass and stone. The produced LC/LU maps along with the sensor and river measurements are used to create flood models, used by public authorities and stakeholders to better understand the area of interest, its needs and the steps needed to support its sustainability.
<p>Mismanaged plastic waste continually accumulates in the marine environment. A large amount of its emission to the ocean originates on land and is transported by rivers, streams and artificial drains. However, monitoring efforts and knowledge building on the dynamics and quantification of these emissions based on field research is scarce and subject to local catchment scale.</p> <p>Here, we present an experimental study of plastic waste transport and retention dynamics in artificial drains (gullies) subject to flash floodings in short drainage areas of Kingston, Jamaica. We developed a novel plastic waste piles survey using UAV and field measurements. The offered investigation has the potential for estimation of plastic waste piles (i) volumes and composition, (ii) transport-retention-remobilization cycles and (iii) correlation with local hydro-meteorology, especially during peak events, where most of the plastic waste is transported.</p> <p>Until now, monitoring efforts were carried out on the lower stretch (1km) of three gullies flowing to Kingston Harbour and the Caribbean Sea during 90 days in the hurricane season of 2021 on a bi-weekly basis. The current dataset includes 24 orthorectified images of the gullies and plastic waste piles. Direct samples of the plastic waste piles are being collected for ground-truth validation. We observe that plastic waste piles are more prominent when large objects (such as refrigerators, tree branches or tyres) are present, forming a base for greater accumulation and affecting remobilization cycles.</p> <p>These results are essential for understanding macroplastic transport processes and the development of innovative technological solutions preventing plastics inflow into the ocean. It has the potential to provide insights into the operational performance before and after the implementation of interception solutions or mitigation measures. Furthermore, it serves as baseline data to strengthen local policy-making on initiatives assessing harmful effects in surrounding ecosystems.</p>
Flood risk prediction requires consistent and accurate sensor measurements, usually provided from traditional in-situ environmental monitoring systems. Crowd-sourced data can complement these official data sources, allowing authorities to improve and fill gaps in the hazard assessment process. However, collecting this information from volunteers, with no technical knowledge and while using low-cost equipment such their smartphones and tablets, raises the question of quality and consistency. To alleviate this barrier two tools were developed in the context of H2020 Scent project. The Water Level Measurement Tool uses image recognition techniques to extract the water level from images containing a measuring tape. The Water Velocity Calculation Tool uses video processing algorithms to extract the water surface velocity from a video containing a pre-defined floating object moving on the surface of a water body. Each extracted measurement is accompanied by a degree of trust. The tools have been designed so that a high degree of trust can be achieved from images and videos taken from regular smartphones. The crowdsourced river measurements are used to develop improved flood models with a dramatically reduced cost as both the measuring tapes and the floating object are low-cost and re-usable while effectively covering large areas of interest.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.