Intelligent daily rainfall prediction for early warning using deep learning and satellite data: application to Bouaflé and Zuénoula stations, Ivory coast

Kamenan, Satti J. R.; Youan, Ta M.; Adja, Miessan G.; Soro, Sandona I.; Kouassi, Amani M.

doi:10.5194/ascmo-12-173-2026

Articles | Volume 12, issue 1

https://doi.org/10.5194/ascmo-12-173-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ascmo-12-173-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 1

08 May 2026

| 08 May 2026

Intelligent daily rainfall prediction for early warning using deep learning and satellite data: application to Bouaflé and Zuénoula stations, Ivory coast

Satti J. R. Kamenan, Ta M. Youan, Miessan G. Adja, Sandona I. Soro, and Amani M. Kouassi

Data sets

KAMENAN/rainfall-prediction-deep-learning-civ: First release – Rainfall prediction model Satti Jean Robert Kamenan https://doi.org/10.5281/zenodo.19669875

Model code and software

KAMENAN/rainfall-prediction-deep-learning-civ: First release – Rainfall prediction model Satti Jean Robert Kamenan https://doi.org/10.5281/zenodo.19669875

Short summary

Recurrent flooding in the Marahoué region, especially at Bouaflé and Zuénoula, requires reliable rainfall forecasts. This study uses deep learning with satellite data to predict daily rainfall up to seven days ahead. The results show high accuracy for short- and medium-term forecasts, supporting early warning systems and helping local communities prepare for flood risks.