Forecasting space–time variability of wave heights in the Bay of Bengal: a genetic algorithm approach

Prediction Method for Ocean Wave Height Based on Stacking Ensemble Learning Model

“…Zhu et al [26] used the back propagation neural network algorithm to predict the effective wave height and the average wave direction. Sinha et al [27] used a genetic algorithm to predict the wave height of the Bay of Bengal, and the results showed that the timeliness performance of the algorithm was better. Nikoo et al [28] proposed, for the first time, to use artificial immune recognition systems for the prediction of effective wave heights in Lake Superior in the northern United States, and their prediction performance was better than that of the artificial neural networks, such as Bayesian networks and support vector machines.…”

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

“…In particular, when point forecasts at specific locations are required, the numerical prediction models are disadvantageous because there are always extremely complex and are highly computer intensive due to the huge amount of input information, such as the vertical profiles of humidity, temperature, and so on. [3,4] It is thus interesting to explore the possibility of predicting the surface wind using only past observations without the need for sophisticated numerical models. Over the years, various such data-adaptive approaches, such as linear regression, support vector machines, [5] artificial neural net-works (ANNs), [6] genetic algorithm (GA), [7] and so on, [8,9] have been proposed for prediction of the nonlinear data series.…”

“…Another advantage is that it provides an explicit analytical forecast equation. [12] The predictive skill of GA has been demonstrated in the cases of sea surface temperature (SST) in the Alboran Sea, [13] summer rainfall over India, [14] SST and sea level anomaly in the Ligurian Sea, [15] wave heights in the north Indian Ocean (NIO), [3,4] the tidal currents in the Arabian Sea, [16] and so on. Meanwhile, ocean surface wind prediction with in situ and scatterometer observations using GA have been tested in the NIO and the results show that predictions with GA made up to three days have been found to be quite encouraging.…”

“…[3] Due to the specific characteristic of the GA algorithm, the prediction models for different point stations and different basins are different. [3,4] In the present study, the GA technique has been used to forecast the surface wind in the South China Sea (SCS), which is an important ocean due to the high level of scientific and economic interests in the area. In a previous diagnostic study, Basu et al [3] explored the ability of predicting the surface wind in the NIO.…”