Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region

Gómez, Derly; Aristizábal, Édier; Aristizábal, Edwin Fabián García; Marín, Diver E.; Valencia, Santiago; Vásquez, Mariana

doi:10.1016/j.jsames.2023.104293

Cited by 3 publications

(2 citation statements)

References 71 publications

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“…From this, it is proposed that the results generated could have the same performance. The results of this study showed the possibility of introducing a simple Bayesian model compared to other methods, such as neural networks [55,65,66] or deep learning approaches [67,68], requiring a more robust landslide inventory, which is not available currently. Although remote sensing data could allow delimitating of the spatial distribution of landslides, the temporal identification is unsuitable due to the long revisit time of satellite products in the Central Andes.…”

Section: Modelingmentioning

confidence: 99%

“…It is important to note that integrating CHIRPS and SRTM may introduce uncertainties due to the different spatial resolutions of these two products. However, despite this consideration, numerous hydrological [51,52] and natural hazard studies [53][54][55] have consistently demonstrated that integrating these sources allows for rigorous analyses with scientific validity.…”

Section: Datamentioning

confidence: 99%

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Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

Maragaño-Carmona,

Fustos Toribio,

Descote

et al. 2023

Water

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The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.

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Section: Modelingmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

Maragaño-Carmona,

Fustos Toribio,

Descote

et al. 2023

Water

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Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

Vega,

Barco,

Hidalgo

2024

Landslides

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According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia.

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Validation of the CHIRPS dataset in a coastal region with extensive plains and complex topography

Arregocés

Rojano

Pérez

2023

Case Studies in Chemical and Environmental Engineering

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Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region

Cited by 3 publications

References 71 publications

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case

Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

Validation of the CHIRPS dataset in a coastal region with extensive plains and complex topography

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