A hybrid neural network model for sea ice thickness forecasting

Sea ice thickness is an important environment load parameter for reliability assessment and life extension decision of existing ageing offshore platforms in ice region. It is a key factor to provide accurate sea ice thickness prediction. Introducing chaos random sequence and immune mechanism into traditional genetic evolution process, Chaos immune genetic optimization algorithm is constructed. Combining the chaos immune genetic optimization algorithm and the BP neural network, a hybrid neural network models is established. The ice thickness in Bohai Sea is predicted using the hybrid neural network model, and a good fitness is revealed between the prediction and practical values. The results show that the hybrid neural network model is feasible and effective. The parameters of Weibull distribution function for ice thickness is estimated, using predicted ice thickness specimens. The assessment load in later service period is updated and more reliable environment load parameters can be provided for ageing platform assessment.