Flood Water Level Modeling and Prediction Using Radial Basis Function Neural Network: Case Study Kedah

Bakar, Mohd Anuar Abu; Aziz, Fathrul Azarshah Abdul; Hussein, Shamsul Faisal Mohd; Abdullah, Shahrum Shah; Ahmad, Fauzan

doi:10.1007/978-981-10-6463-0_20

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…It is officially important that forecasting performance include lead time and error correction is one of the key design criteria, and may dictate the overall design of the model. Despite prior evidence by the researchers [13] and [15], maximum lead time can only adjust for 7 hour and the precise of performance not more than 80%. Hence, the aim of this work is to provide multi-step lead time flood water level forecasting utilizing the upper-river water level station as input employed with NARX and RBF neural network technique.…”

Section: Article Historymentioning

confidence: 91%

“…Flood water level forecasting has been presented recently by [10], the feed forward multilayer perceptron (MLP) with Cuckoo search (CS) algorithm model could perform better for 2 hour ahead of time. Recent studies in Malaysia about disaster risk including floods has been discussed by the researchers [11], [12], [13], [14] and [15]. Studied a predictive and comparative analysis of NARX and Nonlinear Input-Output (NIO) time series prediction has been recently presented by Philip [16].…”

Section: Article Historymentioning

confidence: 99%

“…Radial Basis Function (RBF) is one of the most popular types in neural network that recently became a powerful function or tool uses for approximation, modeling and forecasting [1], [15], time series prediction [21], RBF as surrogate modeling [22], [23] multi-input multi-output (MIMO) control and optimization [24], [25] and classification problem [26].…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

Faruq

Abdullah

Marto

et al. 2018

Int. J. Adv. Intell. Informatics

Self Cite

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Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series of differences between observed and forecast values, are very interesting and challenging task. This paper presents an improved lead time flood forecasting using Non-linear Auto Regressive Exogenous Neural Network (NARXNN), which shows better performance in term of forecast precision and produces minimum error compared to neural network method using Radial Basis Function (RBF) in examined 12-hour ahead of time. First, RBF forecasting model was employed to predict the flood water level of Kelantan River at Kuala Krai, Kelantan, Malaysia. The model is tested for 1-hour and 7-hour ahead of time water level at flood location. The same analysis has also been taken by NARXNN method. Then, a non-linear neural network model with exogenous input promoted with enhancing a forecast lead time to 12-hour. Both about the performance comparison has briefly been analyzed. The result verified the precision of error prediction of the presented flood forecasting model.

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Section: Article Historymentioning

confidence: 91%

Section: Article Historymentioning

confidence: 99%

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

Faruq

Abdullah

Marto

et al. 2018

Int. J. Adv. Intell. Informatics

Self Cite

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“…Compared with the radial basis function neural network (BRFNN) [24] and multi-layer perceptron (MLP) [25] models, ANN with HS and DE was confirmed to show good performance, and it was proved that the ANN model can be used for water flow prediction. In addition, many studies have been conducted to predict key factors of flooding using models such as ANN-based water level prediction models and runoff prediction models [26][27][28][29][30]. However, most of the related studies utilize hydrological data and meteorological data, which are categories of time series data, as input data [31,32].…”

Section: Introductionmentioning

confidence: 99%

Water Level Prediction Model Applying a Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) Method for Flood Prediction

Cho

Kim

Jung

et al. 2022

Water

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The damage caused by floods is increasing worldwide, and if floods can be predicted, the economic and human losses from floods can be reduced. A key parameter of flooding is water level data, and this paper proposes a water level prediction model using long short-term memory (LSTM) and a gated recurrent unit (GRU). As variables used as input data, meteorological data, including upstream and downstream water level, temperature, humidity, and precipitation, were used. The best results were obtained when the LSTM–GRU-based model and the Automated Synoptic Observing System (ASOS) meteorological data were included in the input data when experiments were performed with various model structures and different input data formats. As a result of the experiment, the mean squared error (MSE) value was 3.92, the Nash–Sutcliffe coefficient of efficiency (NSE) value was 0.942, and the mean absolute error (MAE) value was 2.22, the highest result in all cases. In addition, the test data included the historical maximum water level of 3552.38 cm in the study area, and the maximum water level error was also recorded as 55.49, the lowest result. Through this paper, it was possible to confirm the performance difference according to the composition of the input data and the time series prediction model. In a future study, we plan to implement a flood risk management system that can use the predicted water level to determine the risk of flooding, and evacuate in advance.

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“…Neural network models, especially as data-driven approaches, are developed through training the network to demonstrate the relationships and processes that are inherent within the data. Research on flood water level forecasting has been successfully taken by the authors [2,3]. The works are growing advance in exploring more suitable flood forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia

Faruq¹,

Arsa²,

Hussein³

et al. 2020

ISI

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Long short-term memory (LSTM) networks are state of the art technique for time-series sequence learning. They are less commonly applied to the hydrological engineering area especially for river water level time-series data for flood warning and forecasting systems. This paper examines an LSTM network for forecasting the river water level in Klang river basin, Malaysia. The river water level contains of two features dimension and one timeseries observed data, in this study, prediction responses for river water level data using a trained recurrent neural network and update the network state function is applied. The radial basis function neural network (RBFNN) in order to get comparison of the generalization solving problem also performed. The performance indicates with the root mean square error, RMSE 0.0253 and coefficient of determination value, R 2 0.9815 are closely accurate when updating the network state compared with the RBFNN results. These results verified that the LSTM network with specified training set options is a promising alternative technique to the solution of flood modelling and forecasting problems.

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Flood Water Level Modeling and Prediction Using Radial Basis Function Neural Network: Case Study Kedah

Cited by 3 publications

References 9 publications

The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

Water Level Prediction Model Applying a Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) Method for Flood Prediction

Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia

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