Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Gnyawali, Kaushal Raj; Dahal, Kshitij; Talchabhadel, Rocky; Nirandjan, Sadhana

doi:10.1016/j.scitotenv.2023.162242

Cited by 19 publications

(4 citation statements)

References 51 publications

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“…These reactivations are the most common cause of damage in the Champagne vineyards. Triggering them is a complex process that cannot be explained directly by heavy rainfall events [42] but rather by the water content of the sliding masses which is controlled by the slow draining of the Ypresian aquifer. Thus, mass movements do not contain a transmissive aquifer with rheocrene springs: the heterogeneity of the disturbed material impedes the water circulation [10,16,39].…”

Section: Discussionmentioning

confidence: 99%

Internal Structure and Reactivations of a Mass Movement: The Case Study of the Jacotines Landslide (Champagne Vineyards, France)

et al. 2023

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The Jacotines landslide is representative of the large mass movements that affect the Champagne vineyards. Understanding the subsurface structure of these slopes and the mechanisms leading to sliding events is of a great interest, particularly for winegrowers who produce Champagne. This knowledge is generally used to elaborate accurate hazard assessment maps, which is an important feature in land use planning. The approach presented is based on the integration of geophysical imaging (seismic wave velocity and electrical resistivity), lithostratigraphic analysis (drilling core) and geomorphological investigations (surface landforms) to reconstruct the relations between the landslide structure, surface water flow, groundwater regime and the overall slope stability. A first phase of instability resulting in a large rotational slip probably occurred during the Late Glacial Period in morphoclimatic conditions characterized by an excess of water. A second one, still active, leading to superficial reactivations and relates to present hydrogeological conditions determined by the internal structure of the landslide.

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Section: Discussionmentioning

confidence: 99%

Internal Structure and Reactivations of a Mass Movement: The Case Study of the Jacotines Landslide (Champagne Vineyards, France)

et al. 2023

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“…In Thua Thien Hue province, the distance to river value ranged from 0 to 13,818 m. Heavy rains over a short period lead to saturated ground, causing landslides. In addition, precipitation also creates grooves in the surface; this change of the slope morphology can also lead to landslides (Bozzolan et al, 2023; Gnyawali et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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“…US-focused studies include the assessment of structural wildfire impacts on local schools and hospitals [22], hospital exposure to wildfires and coastal flooding at the countylevel [91], and hospital exposure to hurricanes and sea-level rise along the East and South coast [92]. In Nepal, one study highlighted how the education and health sector is an integral part for the identification of critical infrastructure prone to rainfall-triggered landslides [93]. There is a persistent need for more extensive infrastructure layers for hospitals and schools [94], which is only partly met by recent harmonized global datasets for the health and education sector [20], which rely on open-source data that have a known geographical bias in terms of coverage.…”

Section: Social Infrastructurementioning

confidence: 99%

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

Verschuur,

Fernández-Pérez,

Mühlhofer

et al. 2024

PLOS Clim

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Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

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Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Cited by 19 publications

References 51 publications

Internal Structure and Reactivations of a Mass Movement: The Case Study of the Jacotines Landslide (Champagne Vineyards, France)

Internal Structure and Reactivations of a Mass Movement: The Case Study of the Jacotines Landslide (Champagne Vineyards, France)

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

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