Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

Abstract

Salinity intrusion poses significant challenges to coastal regions worldwide. Reliable salinity prediction models can provide valuable information to mitigate the impact and influence of salinity intrusion. However, their accuracy relies mainly on the selected input variables and used optimal models. This study employs the Bayesian
Model Averaging (BMA) algorithm to evaluate input variable importance and select the most reliable salinity
prediction model. Based on an analysis of observed salinity data and climate data extracted from Landsat 8 OLI in the Google Earth Engine platform, the BMA algorithm identifies the significance of critical variables and
optimal salinity prediction models. Various statistical metrics, including R-squared, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) from the Random Forest method, were used to verify the performance of these optimal salinity prediction models. These obtained results offer foundational knowledge and valuable insights for future studies in determining appropriate input variables and selecting the best optimal
salinity prediction model.