Paper Title :Wave Height Forecasting Using Artificial Neural Network And Fuzzy Logic

Author :V. R. Kashikar, S. J. Mane

Article Citation :V. R. Kashikar ,S. J. Mane , (2014 ) " Wave Height Forecasting Using Artificial Neural Network And Fuzzy Logic " , International Journal of Soft Computing And Artificial Intelligence (IJSCAI) , pp. 33-35, Volume-2,Issue-2

Abstract : The present work aims at development of an artificial neural network (ANN) model for forecasting ocean wave height in coastal areas. This study is targeted because these waves are causing a great threat to common man thus disrupting human life. Prediction of significant wave heights (Hs) is of immense importance in ocean and coastal engineering applications. The hourly data set consisting of wave height for 1 year used in the study is collected from National Data Buoy Centre (NDBC) of Station 44013 lying in Boston Area of United States. In order to provide an operational forecasting module for wave height, multilayer perceptron, generalized feed forward and time lag-recurrent network models of artificial neural network and CANFIG Network of Fuzzy Logic are investigated to forecast wave heights. Artificial neural network models based on a three-layered feed forward neural network trained by back-propagation algorithm are investigated and applied. Performance of artificial neural network and Fuzzy Logic for various inputs have been analysed and the results are discussed. The network is trained by different algorithms and it is used to forecast wave height with lead time of 1 hour. The trial and error method is adopted to compare network output with desired output in terms of error statistics viz. R and MSE. The output using generalized feed forward network with conjugate gradient algorithm displayed good results (R=0.98) and (MSE=0.025 m2 ) as compared to CANFIG Network of Fuzzy logic giving (R=0.96) and (MSE=0.047 m2 ). The results obtained shows that artificial neural network can be efficiently used in the analysis and prediction of wave height as compared to Fuzzy Logic.

Type : Research paper

Published : Volume-2,Issue-2

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