Correction to: Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi

Abstract

Data Availability Statement Correction

In the originally published version of this article, the Data Availability Statement did not provide sufficient detail regarding the specific data sources and access information.

To improve transparency and reproducibility, the Data Availability Statement has been updated as follows:

The data supporting the findings of this study are derived from a combination of publicly available datasets and primary research data.

Precipitation (rainfall) data were obtained from the NASA POWER Data Access Viewer, available at: https://power.larc.nasa.gov/data-access-viewer/.

Sea Surface Temperature (SST) data were retrieved via Google Earth Engine from the NOAA Optimum Interpolation Sea Surface Temperature Climate Data Record (NOAA/CDR/OISST/V2.1), covering the period 1990–2024.

Flood vulnerability data were derived from primary survey data collected by the corresponding author as part of doctoral research on flood vulnerability assessment in Malawi, supplemented by regional environmental and hydrological observational data.

The primary survey dataset is not fully publicly available due to ethical and data-sharing considerations; however, it can be made available from the authors upon reasonable request, subject to applicable conditions.

All publicly available datasets can be accessed through the links provided above and are sufficient to support the reproducibility of the study.

This correction does not affect the results or conclusions of the article. The original publication has also been updated.

DOI of original article: https://doi.org/10.30564/jasr.v8i2.10377

Correction Date: 13 April 2026

Refer to: Mwalwimba, I. K., Kalonjeka, B., Msadala, V., Katonda, V., Chisenga, C., Ngongondo, C., & Manda, M. (2025). Deep Learning-based Flood Risk Prediction for Climate Resilience Planning in Malawi. Journal of Atmospheric Science Research, 8(2), 37–50.