Publication:
Time Series Water Level Forecasting Based On Convolutional Neural Network (CNN)

dc.contributor.authorNurul Syafiqah binti Zainien_US
dc.date.accessioned2023-05-03T16:49:06Z
dc.date.available2023-05-03T16:49:06Z
dc.date.issued2020-02
dc.descriptionFYP Sem 2 2019/2020en_US
dc.description.abstractIn Malaysia, floods are a regular natural disaster that occurs during the certain months of the year, usually from October to March due to the monsoon season. Floods occur when the water level rises above the danger stages due to heavy rainfall. Time series forecasting has been performing many years ago and various methods have been study to test their ability. In this study, Convolutional Neural Network (CNN) is suggested to forecast the time series water level. Two types of scenarios are proposed to study the reliability of the proposed model for water level forecasting. Another two methods are proposed namely Artificial Neural Network (ANN) and Long – Short Term Memory (LSTM) for comparison. In this study, result has confirm that LSTM model can achieve better prediction than the proposed model, in terms of Coefficient of determination (R²) and Mean Square Error (MSE) performance measuresen_US
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21410
dc.subjectTime Series Forecastingen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.titleTime Series Water Level Forecasting Based On Convolutional Neural Network (CNN)en_US
dspace.entity.typePublication
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