Height prediction of the Wind – Induced Waves using Adaptive – Network –Based Fuzzy Inference System, Decision Tree and Empirical Methods ( Case study: Bushehr Port) Bushehr Port

Document Type : Research Paper

Authors

1 Master of science coastal Engineering , Khorramshhar university of Marine Sciecne and Technology.

2 Assistant Professor, Department of Maine Structures , Khorramshhar University of Marine Sciecne and Technology

3 Assistant Professor, Department of Maine Structures , Khorramshhar university of Marine sciecne and Technology.

4 Assistant Professor , Department of marine Electronic and communication Engineering , Khorramshhar University of Marine Sciecne and Technology.

Abstract

 Derived Information from the predicted waves will be the basis for engineering design and plays an important role in the construction, maintenance and management of coastal and offshore construction projects. The wind-induced-waves  due to having high energy and frequency are of great importance in the sea. In this research,  the adaptive network based fuzzy inference system was selected to predict the waves’ characteristics.. Study About prediction of wave Characteristics is carried out by several researchers. Zarghani et al  (2006) studied the characteristics of wind-induced waves at the coast of Khark island They used the SPM model to conduct the survey. The results obtained from the model showed that the highest wave was 3.7 m with rotation period of  6.8 seconds. Taleghani and Amirteimori (2008) used the field data of the Caspian Sea waves measured by a waveguide buoy in an artificial neural network. Finally, the comparison of real data measured by the measurement systems with the results of the neural network is a good match that indicates the accuracy and speed of the method used in the short term. Kamranzad and Etemad Shahidi (2011) studied the prediction of wind-induced waves in Assaluyeh using the SWAN numerical model. The validation results of the model showed that the results of calibration model have a good accuracy. The calculated error indices in validation periods also showed the acceptable accuracy of SWAN modeling. Therefore, the constructed model had the ability to predict wave height and period of time in Assalouyeh. In order to do present research, the Adaptive Nero-Fuzzy Inference System as one of the soft computing methods is used. The study area chosen Bushehr and forecasts for the period December 2008 to 2010 is considered. The results showed that, in prediction height of wave induced by wind, accuracy of the Adaptive Nero-Fuzzy Inference System and decision tree are same and are higher than the empirical methods. The comparison results of prediction from empirical methods showed that among the experimental methods, SMB method with high correlation coefficient and less error percent than the other experimental methods has higher accuracy in estimating height of waves in addition the SPM method is not suitable method for determining the height of waves and under estimated the real values.

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References

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Irrigation Sciences and Engineering
Volume 41, Issue 2
June 2018
Pages 151-166

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History

  • Receive Date: 15 October 2016
  • Revise Date: 10 February 2017
  • Accept Date: 19 February 2017
  • Publish Date: 22 June 2018

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