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

Faruq, Amrul; Abdullah, Shahrum Shah; Marto, Aminaton; Bakar, Mohd Anuar Abu; Hussein, Shamsul Faisal Mohd; Razali, Che Munira Che

doi:10.26555/ijain.v5i1.280

Cited by 19 publications

(14 citation statements)

References 26 publications

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“…Small sample sizes have been a serious limitation for many earlier studies. It is agreement as suggested by the work presented in [19], the more input variable could reflect the performance of the model. It is proved that there is an improvement of the result produced by ANFIS in term of more input variables including rainfall and streamflow investigated in the present study.…”

Section: Resultssupporting

confidence: 84%

“…This study is to build multi-time ahead data-driven models with considering multivariable inputs data that enable to simulate and predict river water level from historical-observed data using ANFIS model. The study aimed to expand on the results of previous study [19] in which two machine learning algorithms namely radial basis function and non-linear autoregressive exogenous neural networks have been successfully examined.…”

Section: Introductionmentioning

confidence: 99%

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Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

Faruq

Marto

Izzaty³

et al. 2021

KINETIK

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Intensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning.

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Section: Resultssupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

Faruq

Marto

Izzaty³

et al. 2021

KINETIK

Self Cite

View full text Add to dashboard Cite

show abstract

“…The simulated model is done using neural network deep learning MATLAB® function to train an LSTM network for deep learning. For a better fit and to prevent the training from diverging, standardized the training data is applied to have zero mean and unit of variance as it applied in previous study [2]. The normalized data obtained from the difference between training data and the mean of training data divided by the standard deviation value of training data.…”

Section: Resultsmentioning

confidence: 99%

“…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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“…In a previous study from [4], the authors used the Radial Basis Function Network (RBFN) to achieve the 7-hour water level forecast with the R squared value 82.43 per cent. The short-term water level forecast is about the forecast water level future ahead and the forecast model's performance.…”

mentioning

confidence: 99%

Short-term Water Level Forecast Using ANN Hybrid Gaussian-Nonlinear Autoregressive Neural Network

Wong¹,

Subramaniam²,

Feroz³

et al. 2022

IJIE

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The aim of this study is to develop the best forecast model using hybrid Gaussian-Nonlinear Autoregressive Neural Network to forecast the water level with multiple hoursahead for Melaka River. The developmentof flood forecast models is crucial and has led to risk control, policy recommendations, a reduction in human life loss, and a reduction in flood-related property destruction. In this research, Artificial Neural Network (ANN) approach was used to forecast flood by modeling and forecasting water level time series.ANN approach was selected due to its high reputation abilities to learn from the time-series data pattern. A total of 2782data for the period of one month wasused in ANN training, validation, and testing to forecast the flash flood. In this study,Hybrid Gaussian Nonlinear Autoregressive Neural Network (Gaussian-NAR) was used as the ANN approach to forecasting the water level time series. This study's primary focus is to find the most appropriate forecast model to forecast the water level in multiple time steps ahead, which are 1 hour, 3 hours, 5 hours, and 7 hours. The forecast accuracy measures are measured using the Pearson R and R-squared to find the most accurate model for this multiple time-step ahead. The result indicates that with 7 hours forecast ahead, the R squared is 86.7%. The best model in the Gaussian-NAR forecast is a 3-hour water level forecast with the R squared of 99.8 percent and had the best model performance result.

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

Cited by 19 publications

References 26 publications

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

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

Short-term Water Level Forecast Using ANN Hybrid Gaussian-Nonlinear Autoregressive Neural Network

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