Deep Learning based Multiple Regression to Predict Total Column Water Vapor (TCWV) from Physical Parameters in West Africa by using Keras Library

TL;DR

This paper develops a deep learning multiple regression model using Keras to accurately predict total column water vapor in West Africa based on atmospheric parameters, achieving high accuracy and strong correlation with observed data.

Contribution

It introduces a novel deep learning approach for nonlinear prediction of TCWV using multiple physical parameters with improved accuracy over traditional methods.

Findings

Mean absolute error (MAE) of 3.60 kg/m2

Coefficient of determination (R2) of 0.90

Effective modeling of TCWV with deep learning

Abstract

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.