Climatic Forecasting of Wind and Waves Using Fuzzy Inference Systems

Wind and wave climatic simulations are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. In a climate change perspective, projections of such simulations to a future climate are of great importance for risk management and adaptation purposes. This work investigates the applicability of FIS/ANFIS models for climatic simulations of wind and wave data. The models are coupled with a nonstationary time series modelling, which decomposes the initial time series into a seasonal mean value and a residual part multiplied by a seasonal standard deviation. In this way, the nonstationary character is first removed before starting the fuzzy forecasting procedure. Then, the FIS/ANFIS models are applied to the stationary residual part providing us with more unbiased climatic estimates. Two long-term datasets for an area in the North Atlantic Ocean are used in the present study, namely NORA10 (57 years) and ExWaCli (30 years in the present and 30 years in the future). Two distinct experiments have been performed to simulate future values of the time series in a climatic scale. The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.