  Total column water vapor is an important factor for the weather and climate.
This study apply deep learning based multiple regression to map the TCWV with
elements that can improve spatiotemporal prediction. In this study, we predict
the TCWV with the use of ERA5 that is the fifth generation ECMWF atmospheric
reanalysis of the global climate. We use an appropriate deep learning based
multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level
pressure, Surface pressure, Sea surface temperature, 100 metre U wind
component, 100 metre V wind component, 10 metre U wind component, 10 metre V
wind component, 2 metre dew point temperature, 2 metre temperature. The results
obtained permit to build a predictor which modelling TCWV with a mean abs error
(MAE) equal to 3.60 kg/m2 and a coefficient of determination R2 equal to 0.90.
