Regional landslide evaluation: Two Utah examples

DeGraff, Jerome V.

doi:10.1007/bf02380486

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…Before GIS came to the world, landslide susceptibility maps were usually generated by manually interpreting large‐scale aerial photographs and field survey results (Degraff, 1978; Pomeroy, 1974), which were relatively inefficient and high‐cost. The development of GIS indirectly improved the popularity of machine learning approaches in LSM as it exponentially reduced the difficulties of data processing and therefore improved the efficiency of the task.…”

Section: Data Processingmentioning

confidence: 99%

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

et al. 2023

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Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing to mitigating the social‐eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.

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Section: Data Processingmentioning

confidence: 99%

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

et al. 2023

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“…The Wasatch Plateau has a long history of damaging landslides, including mass movement that evidently has altered the western margin of Emerald Lake (DeGraff, 1978;McDonald and Giraud, 2015). The primary mechanism for historic slope instability is soil saturation caused by either high snowmelt runoff or intense rainfall events (Fleming et al, 1988;Baum and Fleming, 1989).…”

Section: Introductionmentioning

confidence: 99%

Holocene paleohydrology from alpine lake sediment, Emerald Lake, Wasatch Plateau of central Utah, USA

et al. 2022

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Holocene sediments at Emerald Lake in central Utah (3090 m asl) document the paleohydroclimatic history of the western Upper Colorado River headwater region. Multi-proxy analyses of sediment composition, mineralogy, and stable isotopes of carbonate (δ18O and δ13C) show changes in effective moisture for the past ca. 10,000 years at millennial to decadal timescales. Emerald Lake originated as a shallow, closed-basin cirque pond during the Early Holocene. By ca. 7000 cal yr BP, higher lake levels and carbonate δ18O values indicate rising effective moisture and higher proportions of summer precipitation continued at least until ca. 5500 cal yr BP when a landslide entered the lake margin. Between ca. 4500 and 2400 cal yr BP dry conditions at Emerald Lake envelop the timing of the ‘Late Holocene Dry Period’ identified at lower elevations. For the past ca. 2500 years, Emerald Lake δ18O values were relatively low, indicating wetter conditions and higher snow input (compared to rain), except for dry periods at ca. 2000 cal yr BP and during the Medieval Climate Anomaly at ca. 1000 and ca. 500 cal yr BP. Results provide a long-term perspective on precipitation extremes that influence regional water supplies from a snow-dominated catchment typical of the predominant source region for the Upper Colorado River.

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“…To reduce the potential damage and economic losses (Schuster and Fleming, 1986;Kjekstad and Highland, 2009) in urban development and land use planning (Leighton, 1976;Záruba and Mencl, 1982;Alexander, 1986;Alexander, 1989;Alexander, 2005;Petley, 2009) a good support to provide information about landslides is given by landslide susceptibility maps. Susceptibility is the first stage in the process of hazard and risk evaluation (Crozier and Glade, 2005), through which is modeled the relative spatial probability for the occurrence of landslides in a given area (Cararra 1983;Guzzetti, 2006), considering previous information about landslides and their control factors (Carrara and Merenda, 1976;DeGraff, 1978;Kirschbaum et al, 2010;Klose, 2015). To extend the susceptibility into landslide hazard the temporal probability needs to be taken into account (Varnes et al, 1984;Guzzetti et al, 1999;Corominas et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Landslide reactivation susceptibility modeling in Iași Municipality

Necula¹,

Niculiţă

2017

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Iași Municipality as other urban areas around the world has a long history of landslide activity which needs to be studied considering the urban sprawl. We performed a first landslide susceptibility modeling for Iași Municipality based on the AHP method using weights given by expert judgements regarding the influence of preparatory and conditional landslides factors (slope, ruggedness, lithology, historic landslide density and hydrogeology) and weights given by the historic landslide density over the factors. The landslide inventory was performed based on LiDAR data and aerial imagery using the geomorphological mapping of landslide elements. Using the landslide probability density function we have shown that the landslide inventory is valid and we have selected 411 landslides considered recent to be used for the validation. The resulted susceptibility show that the most susceptible to landslide reactivations are the hillslopes of cuesta hills with relict and old landslides, especially at the main scarp. Recent landslides are mainly scarp slumps or landslide body translational reactivations. The validation showed that almost 70% of recent landslides are located on high susceptibility areas. Future work to improve the susceptibility and extend it to hazard modeling is needed considering the long history of landslide reactivations from Iași Municipality and the slow evolution of old landslides like in Țicău neighborhoods.

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Regional landslide evaluation: Two Utah examples

Cited by 14 publications

References 4 publications

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

Holocene paleohydrology from alpine lake sediment, Emerald Lake, Wasatch Plateau of central Utah, USA

Landslide reactivation susceptibility modeling in Iași Municipality

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