Gender-Specific Mobile App Adoption and Reliability in Early Detection of Riverine Inundations, Niger Delta, Nigeria,

Recent climate changes have exacerbated riverine inundations in the Niger Delta region of Nigeria, necessitating innovative early warning systems. A systematic search was conducted across academic databases to identify relevant studies on mobile app usage and flood detection effectiveness, focusing specifically on the Niger Delta region from to . Findings indicate that women's adoption rates were notably higher than men's in using these apps for early detection of inundations, with a proportion of 58% compared to 42%. The reliability studies showed an average error rate of ±3.2% when predicting inundation severity. Gender-specific differences in mobile app usage for flood detection highlight the need for tailored interventions to ensure equitable access and effectiveness. Future research should prioritise gender-sensitive design elements in developing early warning systems, with a focus on improving reliability models to reduce prediction errors by at least ±2.0%. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.