GDL

Journal from gdlhub / 2018-07-25 15:02:21
Oleh : Imam Cholissodin, Maria Tenika Frestantiya, Ziya El Arief, Telkomnika
Dibuat : 2018-07-25, dengan 1 file

Keyword : local global support vector regression; water level; parallel time variant particle swarm optimization;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/6772
Sumber pengambilan dokumen : WEB

Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather.

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