A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks

Hamzić, Adis; Avdagić, Zikrija; Omanović, Samir

doi:10.1109/bihtel.2016.7775713

Cited by 3 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The secondary goal was to inspect how adding new variables impact prediction accuracy in periods of sudden water level change. In [17] it was concluded that the main weakness of single variable time series water level prediction by NAR networks and FFBP networks is slow adjustment to sudden water level changes. This weakness causes large maximal errors in this prediction method.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors

Hamzić

Avdagić

Besic

2020

IJGI

Self Cite

View full text Add to dashboard Cite

Hydropower dam displacement is influenced by various factors (dam ageing, reservoir water level, air, water, and concrete temperature), which cause complex nonlinear behaviour that is difficult to predict. Object deformation monitoring is a task of geodetic and civil engineers who use different instruments and methods for measurements. Only geodetic methods have been used for the object movement analysis in this research. Although the whole object is affected by the influencing factors, different parts of the object react differently. Hence, one model cannot describe behaviour of every part of the object precisely. In this research, a localised approach is presented—two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X axis and the other for the Y axis. Additionally, the prediction of horizontal dam movement is not performed directly from measured values of influencing factors, but from predicted values obtained by machine learning and statistical methods. The results of this research show that it is possible to perform accurate short-term time series dam movement prediction by using machine learning and statistical methods and that the only limiting factor for improving prediction length is accurate weather forecast.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The main weakness of univariate time series water level prediction is slow adjustment to sudden water level changes. [17].…”

mentioning

confidence: 99%

Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors

Hamzić

Avdagić

Besic

2020

IJGI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, NAR networks were used for prediction of water level. In [17], NAR predicts a clearness index that is used to forecast global solar radiations. e NAR model is based on the feedforward multilayer perceptron model with two inputs and one output [18].…”

Section: Related Workmentioning

confidence: 99%

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

Asad

Qamar

Abbas

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Diabetes type 1 is a chronic disease which is increasing at an alarming rate throughout the world. Studies reveal that the complications associated with diabetes can be reduced by proper management of the disease by continuously monitoring and forecasting the blood glucose level of patients. Objective. The prior prediction of blood glucose level is necessary to overcome the lag time for insulin absorption in diabetic type 1 patients. Method. In this research, we use continuous glucose monitoring (CGM) data to predict future blood glucose level using the previous data points. We compare two neural network techniques. We apply the optimal feedforward neural network and then propose optimal nonlinear autoregressive neural networks for blood glucose prediction 15–30 minutes earlier for diabetic type 1 patients. We validate the proposed model with 2 virtual subjects using their 24-hour blood glucose level data. These two case studies have been compiled from AIDA, i.e., the freeware mathematical diabetes simulator. Results. In the prediction horizon (PH) of 15 and 30 minutes, improved results have been shown for minimal inputs for blood glucose level of a particular subject. Root mean square error (RMSE) is used for performance calculation. For the optimal feedforward neural network, the RMSE is 0.9984 and 3.78 ml/dl, and for the optimal nonlinear autoregressive neural network, it reduces the RMSE to 0.60 and 1.12 ml/dl for 15 min and 30 min prediction horizons, respectively, for subject 1. Similarly, for subject 2 for the optimal feedforward neural network, RMSE is 1.43 and 3.51 ml/dl which is improved using the optimal autoregressive neural network to 0.7911 and 1.6756 ml/dl for 15 min and 30 min prediction horizons, respectively. Validation. We further validate our proposed model using UCI machine learning datasets (Abalone and Servo), and it shows improved results on that as well. Conclusion and Future Work. The proposed optimal nonlinear autoregressive neural network model performs better than the feedforward neural network model for these time series data. In the future, we intend to investigate a greater collection of AIDA scenarios and data that are real and influence other factors of BGLs.

show abstract

“…The prediction results produce an r-value of 0.9567 for prediction, and the author proves that the NAR model has good accuracy compared to the tidal harmonic analysis method. [7] proposed an approach using the NAR for the short-term level forecast. The author also compares these two ANN models: the NAR model and the Feed Forward Back Propagation (FFBP) neural networks.…”

mentioning

confidence: 99%

“…In this study, the author used NAR prediction to forecast the number of COVID-19 cases for the next 50 days. A study conducted by [7] used the NAR method for crop evapotranspiration prediction at Kanchipuram, India. This prediction will allow the reliable project planning and operation of the irrigation system in that particular area.…”

mentioning

confidence: 99%

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

Wong¹,

Subramaniam²,

Feroz³

et al. 2022

IJIE

View full text Add to dashboard Cite

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.

show abstract

A sequential approach for short-term water level prediction using nonlinear autoregressive neural networks

Cited by 3 publications

References 9 publications

Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors

Multistage Cascade Predictor of Structural Elements Movement in the Deformation Analysis of Large Objects Based on Time Series Influencing Factors

Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks

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

Contact Info

Product

Resources

About