Research on inshore depth inversion method combined with Bayesian feature selection
Aiming at the problems of long acquisition period and high risk of existing offshore water depth data,it is difficult to meet the high time resolution measurement of large-scale offshore water depth data.Taking the Liaodong Bay area as the research area,this paper proposes an improved LightGBM offshore water depth inversion method combined with Bayesian feature selection.Firstly,13 bands and band ratio features are selected according to Bayesian feature selection method.Secondly,the weight of input variables is calculated based on the geospatial weighting module.Finally,based on the LightGBM inversion module,the inversion of water depth data is studied.In this paper,200 data samples from different spatial locations in Liaodong Bay are selected as the test set to verify the accuracy of the method and LightGBM model in the offshore water depth inversion task.The results show that the Pearson correlation coefficient(r)value of this method is 0.946,the root mean square error(RMSE)is 0.265 meters,the deviation bias²is 0.017 and the mean absolute percentage error MAPE is 0.031.The inversion accuracy and stability are better than the Classic LightGBM model,which can be applied to the inversion of offshore water depth.
water depth inversionBayesian feature selectionLiaodong BayLightGBM