On-line wave prediction |
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| Institution: | 1. Central Water and Power Research Station, Khadakwasla, Pune 411 024, India;2. Indian Institute of Technology, Mumbai 400 076, India;1. Michigan Technological University, MI, USA;2. Iowa State University, IA, USA;1. Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Spain;2. Department of Physics, Universidad de Las Palmas de Gran Canaria, Gran Canaria, Spain;1. School of Agricultural, Computational, and Environmental Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia;2. Department of Science, School of Science and Technology, The University of Fiji, Saweni, Lautoka, Fiji;1. College of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China;2. Qingdao Innovation and Development Base of Harbin Engineering University, Harbin Engineering University, Qingdao, 266000, China;1. College of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China;2. Department of Military Oceanography and Hydrography, Dalian Navy Academy, Dalian, 116018, China;3. Qingdao Graduate School, Harbin Engineering University , Qingdao, 266400, China |
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| Abstract: | Operational prediction of wave heights is generally made with the help of complex numerical models. This paper presents alternative schemes based on stochastic and neural network approaches. First order auto regressive moving average and auto regressive integrated moving average type of models along with a three-layered feed forward network are considered. The networks are trained using three different algorithms to make sure of the correct training. Predictions over intervals of 3, 6, 12 and 24 h are made at an offshore location in India where 3-hourly wave height data were being observed. Comparison of model predictions with the actual observations showed generally satisfactory performance of the chosen tools. Neural networks made more accurate predictions of wave heights than the time series schemes when shorter intervals of predictions were involved. For long range predictions both the stochastic and neural approaches showed similar performance. Small interval predictions were made more accurately than the large interval ones. |
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