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Reducing computational time of closed-loop weather monitoring: a complex event processing and machine learning based approach

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dc.contributor.author Chandrathilake, HMC
dc.contributor.author Hewawitharana, HTS
dc.contributor.author Jayawardana, RS
dc.contributor.author Viduranga, ADD
dc.contributor.author Bandara, HMND
dc.contributor.author Marru, S
dc.contributor.author Perera, S
dc.contributor.editor Jayasekara, AGBP
dc.contributor.editor Bandara, HMND
dc.contributor.editor Amarasinghe, YWR
dc.date.accessioned 2022-09-08T07:52:46Z
dc.date.available 2022-09-08T07:52:46Z
dc.date.issued 2016-04
dc.identifier.citation H. M. C. Chandrathilake et al., "Reducing computational time of closed-loop weather monitoring: A Complex Event Processing and Machine Learning based approach," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 78-83, doi: 10.1109/MERCon.2016.7480119. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/18984
dc.description.abstract Modern weather forecasting models are developed to maximize the accuracy of forecasts by running computationally intensive algorithms with vast volumes of data. Consequently, algorithms take a long time to execute, and it may adversely affect the timeliness of forecast. One solution to this problem is to run the complex weather forecasting models only on the potentially hazardous events, which are pre-identified by a lightweight data filtering algorithm. We propose a Complex Event Processing (CEP) and Machine Learning (ML) based weather monitoring framework using open source resources that can be extended and customized according to the users’ requirements. The CEP engine continuously filters out the input weather data stream to identify potentially hazardous weather events, and then generates a rough boundary enclosing all the data points within the Areas of Interest (AOI). Filtered data points are then fed to the machine learner, where the rough boundary gets more refined by clustering it into a set of AOIs. Each cluster is then concurrently processed by complex weather algorithms of the WRF model. This reduces the computational time by ~75%, as resource heavy weather algorithms are executed using a small subset of data that corresponds to only the areas with potentially hazardous weather. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.uri https://ieeexplore.ieee.org/document/7480119 en_US
dc.subject complex event processing en_US
dc.subject machine learning en_US
dc.subject weather monitoring en_US
dc.title Reducing computational time of closed-loop weather monitoring: a complex event processing and machine learning based approach en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Engineering Research Unit, University of Moratuwa en_US
dc.identifier.year 2016 en_US
dc.identifier.conference 2016 Moratuwa Engineering Research Conference (MERCon) en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 78-83 en_US
dc.identifier.proceeding Proceedings of 2016 Moratuwa Engineering Research Conference (MERCon) en_US
dc.identifier.doi 10.1109/MERCon.2016.7480119 en_US


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