Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

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

Domain:

climate

Record type:

paper
Creator:
Diouf, DaoudaNiaThi
Publisher:
arXiv
Host:avatar
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. 8pages, 6 figures, 3 tables

Visit

doi.orgarxiv.org

Tags

Signal Processing (eess.SP)Neural and Evolutionary Computing (cs.NE)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineeringFOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

Similar

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approachabdoul-gambo/Deep-Learning-examples-KerasMODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEEP LEARNING METHODS: A COMPARISON OF MULTIPLE ALGORITHMSComparison of Multiple Linear Regression and Artificial Neural Network Models in retrieving Water Quality Parameters using Remotely Sensed Data: Lake Victoria (Tanzanian) WaterA Deep Learning Approach to Predict Blood Pressure from PPG SignalsA Multiple Linear Regression Model to Predict the Price of Cement in Nigeria

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

West African food systems and rural socio-economics are based on rainfed agriculture, which makes so

abdoul-gambo/Deep-Learning-examples-Keras

## Deep Learning Demo In this post, I show an example of using deep learning techniques to identify

MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEEP LEARNING METHODS: A COMPARISON OF MULTIPLE ALGORITHMS

Deep learning provide successful applications in many fields. Recently, machines learning a

Comparison of Multiple Linear Regression and Artificial Neural Network Models in retrieving Water Quality Parameters using Remotely Sensed Data: Lake Victoria (Tanzanian) Water

Water is an essential resource for the survival and well-being of humans and ecosystems; hence, the

A Deep Learning Approach to Predict Blood Pressure from PPG Signals

A Deep Learning Approach to Predict Blood Pressure from PPG Signals

Poster presented at the Deep Learning Indaba 2022 by Dylan Geldenhuys

A Multiple Linear Regression Model to Predict the Price of Cement in Nigeria

This study investigated factors affecting the price of cement in Nigeria, and developed a mathematic