Flood water level modeling and prediction using NARX neural network: Case study at Kelang river

Ruslan, Fazlina Ahmat; Samad, Abd Manan; Zain, Zainazlan Md; Adnan, Ramli

doi:10.1109/cspa.2014.6805748

Cited by 43 publications

(19 citation statements)

References 17 publications

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“…This model was developed using MATLAB neural network toolbox. Rainfall intensity is based on various factors (variables) like pressure, temperature, wind, speed and direction, therefore the flood forecast warning system must be very accurate and intelligent to provide an initial warning [32]. The procedure was an extension of ARX model; five inputs were feed to the NNARX model to guess the hazard (flood water level).…”

Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

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Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

et al. 2020

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“…Artificial neural networks (ANNs) are one approach to artificial intelligence (AI) [25], [26] which simulate the behavior of biological brains based on a system of interconnected neurons used to compute values from inputs. They represent a form of machine learning, which have proven particularly effective in pattern recognition.…”

Section: E Artificial Neural Networkmentioning

confidence: 99%

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

2020

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The global positioning system is the most popular outdoor positioning system; however, it is inapplicable to indoor environments. In this study, we created a hybrid algorithm, which applies an artificial neural network to resolve the problems of indoor positioning and a swarm-intelligence algorithm to perform the time-consuming task of adjusting parameters. Experiment results demonstrate that the positioning accuracy of the proposed hybrid algorithm is equivalent to the exhaustive method with search times that are far shorter.

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“…Over the past decades, ANN had been successfully applied to predict the flood discharge [1][2][3]. An important factor that influenced the widely used ANN model in flood prediction was the input-output relationship of the ANN model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of flood water level prediction using improved RBFNN structure

Adnan

Samad

Tajjudin

et al. 2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Recently, the applications of Artificial Neural Network (ANN) in various hydrologic problems have becoming popular. This is due to ability of ANN models to estimate nonlinear functions and hence become important tools to solve diverse water resources problems. Particularly, ANN models have been used in hydrological fields such as river flow forecasting, rainfall-runoff estimation, flood prediction and water quality prediction. Therefore, this paper proposed flood water level prediction model using Radial Basis Function Neural Network (RBFNN) and Improved RBFNN structure that using the water level data from Kelang river which is located at Jambatan Petaling, Kuala Lumpur. The models were developed by processing offline data over time using neural network architecture. The methodologies and techniques of the two models were presented in this paper and comparison of the long term runoff time prediction results between them were also conducted. The prediction results of the Radial Basis Function Neural Network architecture indicate fair performance for the one hour ahead of time prediction. The performance indices results also concluded that the Improved RBFNN model was more reliable than that of the original RBFNN model.

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Flood water level modeling and prediction using NARX neural network: Case study at Kelang river

Cited by 43 publications

References 17 publications

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

Investigation of Flash Floods on Early Basis: A Factual Comprehensive Review

Indoor Positioning System Using Artificial Neural Network With Swarm Intelligence

Modeling of flood water level prediction using improved RBFNN structure

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