Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Sajinkumar, K.S.; Rinu, S.; Oommen, Thomas; Vishnu, C.L.; Praveen, K. R.; Rani, V. R.; Muraleedharan, C.

doi:10.1007/s11069-020-04004-w

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…However, such models for a slope stability evaluation require knowledge about the mechanical and hydraulic parameters of the soils, lithostratigraphy and morphology from the site characterization, and monitoring and laboratory tests [106]. Several recent studies aimed to improve the effectiveness of rainfall thresholds for landslides by considering the soil characteristics, such as soil depth or soil moisture [107][108][109]. The results of the present study showed that the information on soils that is already available in regional and national databases allows us to define rainfall thresholds more accurately than empirical thresholds based solely on the meteorological conditions leading to the triggering of shallow landslides.…”

Section: Discussionmentioning

confidence: 99%

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Luino

Graff

Biddoccu³

et al. 2022

Land

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Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study and analysis of shallow landslides and mud-debris flows triggered by rainfall events in Lombardy was carried out for the period of 1911–2010, over an area of 14,019 km2. In this study, intensity–duration rainfall thresholds have been defined using the frequentist approach, considering some pedological characteristics available in regional soil-related databases, such as the soil region, the textural class, and the dominant soil typological units (STU). The soil-based empirical rainfall thresholds obtained considering the soil regions of the study area were significantly different, with a lower threshold for landslide occurrence in the soil region M1 (Alps), where soils developed over siliceous parent material, with respect to the whole study area and the soil region M2 (Prealps), where soils developed over calcareous bedrocks. Furthermore, by considering textural classes, the curves were differentiated, with coarse-textured soils found more likely to triggerlandslides than fine soils. Finally, considering both texture and main soil groups, given the same rainfall duration, the rainfall amount and intensity needed to initiate a landslide increased in the following order: “coarse-skeletal” Cambisols < Umbrisols < Podzols < “fine” Cambisols. The results of this study highlighted the relevant role of pedological conditioning factors in differentiating the activation of rainfall-induced shallow landslides in a definite region. The information on soils can be used to define more precise rainfall–pedological thresholds than empirical thresholds based solely on meteorological conditions, even when they are locally defined. This knowledge is crucial for forecasting and preventing geo-hydrological processes and in developing better warning strategies to mitigate risks and to reduce socio-economic damage.

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Section: Discussionmentioning

confidence: 99%

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Luino

Graff

Biddoccu³

et al. 2022

Land

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“…Sengupta et al 2010) and for population of landslides (e.g. Sajinkumar et al 2020) and using both rain gauges and satellite data. Examples of rainfall thresholds defined using ground-based rainfall data are found in Uttarakhand (Kanungo and Sharma 2014), Sikkim (Sengupta et al 2010;Harilal et al 2019), West Bengal, particularly in Darjeeling (Dikshit and Satyam 2019;Dikshit et al 2020a) and Kalimpong (Dikshit and Satyam 2018;Teja et al 2019;Abraham et al 2020), Tamil Nadu (Jaiswal and van Westen 2009;Thennavan et al 2020), and Kerala (Naidu et al 2018;Abraham et al 2019Abraham et al , 2021.…”

Section: Introductionmentioning

confidence: 99%

Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India

Gariano

Melillo

Brunetti

et al. 2023

Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022

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In India, rainfall-induced landslides cause a high toll in terms of fatalities and damages. Therefore, the adoption of tools to predict the occurrence of such phenomena is urgent. For the purpose, the LANDSLIP project aimed at developing a landslide early warning system (LEWS) to forecast the occurrence of rainfall-induced landslides in two Indian pilot areas: Darjeeling and Nilgiris. Rainfall thresholds are a widely used tool to define critical probability levels for the possible occurrence of landslides in large areas, and are particularly suitable to be implemented in LEWSs.In this work, we exploited two catalogues of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively. Adopting a frequentist statistical method and using an automatic tool, we determined rainfall thresholds at different non-exceedance probabilities for the two pilot areas. Despite the daily temporal resolution of rainfall data and the spatial and temporal distribution of the documented landslides, the thresholds calculated for the two areas have acceptable uncertainties and were implemented in the LANDSLIP LEWS prototype. We expect that the new thresholds and the whole system will contribute to mitigate the landslide risk in the study areas.

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“…Among the machine learning models used to calculate susceptibility, we find logistic regression, support vector machines (SVM), decision trees, k nearest neighbors (KNN), neural networks [25], Bayesian network [26], naive bayes [27] or fuzzy logic [28]. Although in recent times, research has also been focused on the use of assembler techniques, such as bagging, dagging, boosting [29], the use of deep neural networks (deep learning) [30] or the application of hybrid computational intelligence models [10,31].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment Using an AutoML Framework

Bruzón

Arrogante-Funes

et al. 2021

IJERPH

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The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.

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Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Cited by 8 publications

References 26 publications

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Challenges in Defining Frequentist Rainfall Thresholds to Be Implemented in a Landslide Early Warning System in India

Landslide Susceptibility Assessment Using an AutoML Framework

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