Estimates of Landslide Numbers as a Function of Accumulated Precipitation Using Linear Regression Models
DOI:
https://doi.org/10.11137/1982-3908_2025_48_59636Keywords:
Landslide prediction, Covariate interaction models, Rainfall intensity-duration productAbstract
This study develops linear regression models incorporating covariate interaction terms to estimate landslide frequency as a function of cumulative precipitation in Rio de Janeiro, Brazil, under varying temporal distributions of landslides during critical rainfall episodes. Leveraging landslide inventories and high-resolution precipitation data (2000–2005) from the Alerta-Rio monitoring system, the models were calibrated using pluviometric predictors, including hourly intensity, 24-hour cumulative rainfall (R24), and 96-hour cumulative rainfall (R96). A non-linear interaction term, termed rainfall power (defined as the product of hourly rainfall intensity and R24), was introduced to capture intensity-duration dependencies. Results demonstrated significant explanatory power, with coefficients of determination (R2) exceeding 50% across models. The rainfall power-integrated model achieved high performance (R2>90%; p<0.001), underscoring the critical role of non-linear hydro-meteorological interactions in landslide triggering mechanisms. While linear frameworks provided a baseline for risk estimation, residual analysis revealed systematic biases in scenarios with high precipitation variability, necessitating advanced non-linear methodologies (e.g., Poisson regression or generalized linear models with log-link functions). These findings highlight the potential of covariance-driven regression for operational nowcasting systems, enabling real-time landslide risk quantification. However, model generalizability remains constrained by the omission of geo-environmental covariates (e.g., soil saturation thresholds, slope stability indices). Future work should prioritize multi-hazard validation using post-2005 extreme events and integrate geospatial data layers to refine spatial-explicit risk forecasts. This approach bridges empirical rainfall-landslide correlations with scalable early-warning frameworks, advancing disaster preparedness in tropical urban environments.
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