A GIS-based software for forecasting pollutant drift on coastal water surfaces using fractional Brownian motion: A case study on red tide drift |
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| Affiliation: | 1. Department of Computing, Sheffield Hallam University, UK;2. School of Computing, Plymouth University, UK;3. School of Computing, Plymouth University, UK;1. Department of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, Tamilnadu, India;7. Department of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, Tamilnadu, India;1. Department of Physiology, School of Medical Sciences, Yanbian University, Yanji 133-002, China;2. Key Laboratory of Organism Functional Factors of the Changbai Mountain, Ministry of Education, Yanbian University, Yanji 133-002, China;3. Institute of Clinical Medicine, Yanbian University, Yanji 133-000, China;4. Cellular Function Research Center, Yanbian University, Yanji 133-002, China;5. Food Research Center, Yanbian University, Yanji 133-002, China;6. Department of Biology, School of Medicine Sciences, Dalian University, Dalian, China;1. College of Computer Science and Engineering, Northeastern University, Shenyang, China;2. College of Software, Northeastern University, Shenyang, China;3. Department of Computer Science, Liverpool John Moores University, Liverpool, UK |
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| Abstract: | For ocean pollution emergencies, decision-makers need to quickly know the location of the pollutant for quick assessment and response strategies. In this study, an integrated operational forecasting model coupling a non-Fickian particle-tracking diffusion model based on fractional Brownian motion and geographic information system (GIS) has been developed to implement an operating system for pollutant drift forecasting. The software was developed in C# and C++ language using ArcGIS Engine functions which provides improved visualization and user-friendly and automatic tools for simulation in a geographically referenced environment. The capabilities and effectiveness of the developed software were illustrated by predicting red tide drift through calibration with field observations. This visualized operational forecasting software provides a quick and easy deployable tool for decision-makers in quick response to emergency ocean pollution events. |
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| Keywords: | Pollutant drift Fractional Brownian motion GIS Particle-tracking Visualization Red tide |
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