Geomorphological tools for mapping natural hazards

Chelli, Alessandro; Bordoni, Massimiliano; Cappadonia, Chiara; Pepe, Giacomo; Rotigliano, Edoardo; Smith, Mike

doi:10.1080/17445647.2021.1920794

Cited by 13 publications

(4 citation statements)

References 26 publications

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“…Geomorphological maps provide information on the relief, landforms and genesis of the earth's surface, and have been developed all around the world (Bishop et al, 2012; Paron & Claessens, 2011; Verstappen, 2011). Their use is not only of scientific significance, but is also important for all kinds of spatial developments at the regional scale, such as natural hazard mapping (Chelli et al, 2021), urban development (Douglas, 2020), archaeological prospecting (Van Lanen et al, 2015), land‐use planning (Bocco et al, 2001; Roccati et al, 2020) and climate adaptation (Wageningen University and Research, 2020). However, geomorphological mapping is time‐consuming and expensive, done by well‐trained geomorphologists, and requires high time investment.…”

Section: Introductionmentioning

confidence: 99%

Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time-consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi-automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data.We selected a test area in the Dutch push-moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques.

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Section: Introductionmentioning

confidence: 99%

Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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“…Geomorphic hazards are hazards that are related to the natural processes of the earth, such as landslides, floods and erosion (Chelli et al 2021;Keller and DeVecchio 2019). These hazards are influenced by a range of factors, including climate, geology, topography, and land use.…”

Section: The Interconnection Between Geomorphic Hazards and Climate C...mentioning

confidence: 99%

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Mir,

Patel

2024

JWMM

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The hydrological channel networks of the steep mountains are extensively designated and organized geographic systems and are very complicated. They are composed of granular beds that are uneven and are subjected to fluid forces that fluctuate spatially and temporally. The flow and movement of sediment in these streams are significantly shaped by large rocks such as boulders. However, it is difficult to comprehend these mountain streams because significant information is unavailable regarding these channels as compared to plane bed streams. To address this issue, a critical review of the numerical, computational fluid dynamics, and machine learning mechanisms underlying sediment transport across different flow conditions in steep channels is presented while considering the current and foreseeable conditions for various sediment transport phenomena. The present study emphasized to carry out further analysis on these steep channels using advanced available techniques to get an insight into the morphology of these channels. Furthermore, the hazards associated with steep mountain channels are reviewed as they have a significant impact on infrastructure and habitation in mountainous regions.

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“…The fundamental basis for any landslide study is geological, geomorphological, and engineering geological field mapping. Modern geomorphological mapping is strongly set within the technical capabilities of multimedia, digital data, and imagery at different scales, including the presentation of geomorphological mapping using GIS, DEMs, and color (Chelli et al 2021). The most commonly mapped features are landforms, and their morphogenesis, morphochronology, morphodynamics, and morphometry provide an understanding of the landscape and the processes that formed and continue to modify it.…”

Section: Mappingmentioning

confidence: 99%

Landslide monitoring techniques in the Geological Surveys of Europe

et al. 2023

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Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020).

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Geomorphological tools for mapping natural hazards

Cited by 13 publications

References 26 publications

Comparing geomorphological maps made manually and by deep learning

Comparing geomorphological maps made manually and by deep learning

A Comprehensive Review on Sediment Transport, Flow Dynamics, and Hazards in Steep Channels

Landslide monitoring techniques in the Geological Surveys of Europe

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