Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

Karimi, Shahram; Kisi, Özgür; Shiri, Jalal; Makarynskyy, Oleg

doi:10.1016/j.cageo.2012.09.015

Cited by 109 publications

(49 citation statements)

References 27 publications

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“…Sudheer et al [30] have used the statistical properties for the input data to select the standard input time delay of the model. In addition, multi linear regression (MLR) model isused to evaluate the degree of effect of each variable and to select the most effective input continues monitoring data [31,32]. Therefore, MLR model selection delay is applied in this study to select the effective time delay.…”

Section: Anfis Movement Model Identificationmentioning

confidence: 99%

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Kaloop

Sayed

2015

IJGI

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This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS) technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS) time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1)the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2) the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and 2340(3) The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

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Section: Anfis Movement Model Identificationmentioning

confidence: 99%

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Kaloop

Sayed

2015

IJGI

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“…Gauci et al [34] also used past measurements of HFR data and satellite wind observations to fill gaps of HFR data with ANN technique. Karimi et al [35] used previous sea level values to establish ANN and ANFIS models. Karimi et al [35] found that ANN and ANFIS models gave similar forecasts and outperformed auto-regression moving average models (ARMA) for all the prediction intervals.…”

Section: Introductionmentioning

confidence: 99%

“…Karimi et al [35] used previous sea level values to establish ANN and ANFIS models. Karimi et al [35] found that ANN and ANFIS models gave similar forecasts and outperformed auto-regression moving average models (ARMA) for all the prediction intervals. Frolov et al [36] developed a statistical model for predicting surface currents based on historical HFR observations and forecasted winds.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Ren

Hartnett

2018

Remote Sensing

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Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approach-Artificial Neural Networks (ANN)-was applied to predict surface currents in a tide-and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation (≥0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model's peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind-and tide-dominated currents using ANN.

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“…Recently, many integrated methods were developed to predict the WLC (Karimi et al 2013;El-diasty & Al-harbi 2015;. For example, Karimi et al (2013) developed a prediction model that investigated the neuro-fuzzy inference system and artificial neural network (ANN) to predict the WLC and the results showed that the two models performance are similar to detect the WLC. investigated the autoregressive moving average (ARMA) to model predict the WLCs for Alexandria tide gauge station in Egypt.…”

Section: Introductionmentioning

confidence: 99%

“…Morever, Kisi et al (2012) showed that the three models provide accurate prediction results with the maximum variation of the root-meansquare error (RMSE) is 9 mm. In addition, Karimi et al 2013 utilized the neuro-fuzzy inference system to predict a different periods of the water level measurements and they found that the accuracy of model is increased with short measurement period of water level. Kisi et al (2012) and Kisi et al (2015) concluded that due to the limit number of produce underestimated of the parameters of prediction models design, a high precision of the prediction water level models can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Real-time prediction of water level change using adaptive neuro-fuzzy inference system

Kaloop

El-Diasty

2017

Geomatics, Natural Hazards and Risk

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Accurate water levels modelling and prediction is essential for maritime applications. Water prediction is traditionally developed using the leastsquares-based harmonic analysis method based on water level change (WLC) measurements. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the wavelet neural network (WNN) has recently been developed for the WLC prediction from short water level measurements. However, a new adaptive neuro-fuzzy inference system (ANFIS) model is proposed and developed in this paper. The ANFIS model is utilized to predict and select the WLC models of one month of hourly WLC for Yarmouth, Sain-John and Charlottetown stations in Canadian waters and compared with the current-state-of-the-art WNN model. The statistical analysis is applied to analyse the performance of the developed model in training and testing stages. The results showed an accurate modelling level using ANFIS technique for each station in training and testing stage. A comparison between the developed ANFIS method and the current-state-of-the-art WNN method shows that the accuracy of the developed ANFIS model is superior to the current-state-of-the-art model by 21.5% in average.

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Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

Cited by 109 publications

References 27 publications

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

Real-time prediction of water level change using adaptive neuro-fuzzy inference system

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