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 |