2012 IEEE Control and System Graduate Research Colloquium 2012
DOI: 10.1109/icsgrc.2012.6287127
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Flood water level modelling and prediction using artificial neural network: Case study of Sungai Batu Pahat in Johor

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Cited by 35 publications
(19 citation statements)
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“…The results indicated using RNN to forecast natural inflows into hydropower reservoirs outperformed the traditional stochastic and conceptual models. Adnan et al [20] predicted the flood water level using Back Propagation Neural Network (BPN) for flood disaster mitigation. BPN with an extended Kalman filter showed significant improvement to the prediction of the flood water level.…”
Section: Grnn [Generalized Regressionmentioning
confidence: 99%
See 1 more Smart Citation
“…The results indicated using RNN to forecast natural inflows into hydropower reservoirs outperformed the traditional stochastic and conceptual models. Adnan et al [20] predicted the flood water level using Back Propagation Neural Network (BPN) for flood disaster mitigation. BPN with an extended Kalman filter showed significant improvement to the prediction of the flood water level.…”
Section: Grnn [Generalized Regressionmentioning
confidence: 99%
“…Adnan et al [20] mentioned nonlinear models, such as support vector regression (SVR) outperformed linear models or time series models in the prediction of streamflow.…”
Section: Grnn [Generalized Regressionmentioning
confidence: 99%
“…Chen et al used an improved genetic algorithm coupling a back propagation neural network (IGA-BPNN) model for water level prediction, and they used the ground station data of the Hanjiang River as the input of the model [1]. Ramli Adnan et al introduced the extended Kalman filter at the output of the BPNN to show the improvement of the prediction of the actual flood level [19], and he also used the data of upstream and downstream stations of the river as the input. Although many people use site data modeling, there are many areas with sparse or even no site records, which makes it hard to apply the data-driven method.…”
Section: Introductionmentioning
confidence: 99%
“…In recent times, it has become possible to take into account all the factors affecting water level fluctuation, due to the development of computer technology for processing big data. ANN is widely used to forecast the groundwater level [32], water level in rivers [33][34][35], reservoirs [36][37][38], and wetlands [39,40]. However, research into water level prediction in wetlands is more recent than other areas of study, with the research focusing on the use of ANN [39,40].…”
Section: Introductionmentioning
confidence: 99%