Snow avalanche susceptibility of the Circo de Gredos (Iberian Central System, Spain)

Soteres, Rodrigo L.; Gilsanz, Javier de Pedraza; Carrasco, Rosa M.

doi:10.1080/17445647.2020.1717655

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…The literature [10] gives qualitatively relevant operations and proofs for hesitant fuzzy language-valued sets and probabilistic inference steps based on hesitant fuzzy languagevalued sets and triangular fuzzy numbers, which flexibly reflect the uncertainty of events. The literature [11] improves the G&M algorithm by proposing algorithms that are easier to implement and have substantially higher efficiency and accuracy. The literature [12] proposes a method in the EMFlow algorithm set to compress memory before writing to limit the amount of memory written and then reads to achieve locality by evicting the least-used blocks from memory using a least recently used (LRU) caching policy.…”

Section: Related Studiesmentioning

confidence: 99%

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Zhang¹,

Liu

2021

Advances in Mathematical Physics

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With the rapid development of information society, a large amount of vague or uncertain English interpretation information appears in daily life. Uncertain information processing is an important research content in the field of artificial intelligence. In this paper, we combine the three-branch concept lattice and linguistic values with the digital elevation model and propose the three-branch fuzzy linguistic concept lattice as well as the attribute approximation method. In this paper, an improved serial algorithm for sink accumulation is proposed. The improved algorithm changes the order of cell calculation; after the cumulative amount of a “sub-basin” is calculated, all cells of the next “sub-basin” are calculated until all cells are calculated. The improved algorithm reduces the overhead space in the calculation process, reduces the pressure of cells entering and leaving the queue, and improves the calculation efficiency. The improved cumulant algorithm is compared with the commonly used recursive cumulant algorithm and the nonrecursive cumulant algorithm, and the improved algorithm improves by about 17% compared with the nonrecursive algorithm at 106 cell level, and the computation time of the recursive algorithm is about 3 times of the improved algorithm. Because the sink accumulation serial algorithm is an important part of the parallel calculation of sink accumulation, and the execution time is shorter by using the improved algorithm, this paper applies the proposed improved accumulation serial algorithm to the process of parallel calculation of accumulation.

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Section: Related Studiesmentioning

confidence: 99%

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Zhang¹,

Liu

2021

Advances in Mathematical Physics

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“…Although almost all the works on the glacial morphology of High Gredos Massif contain some kind of graph, scheme or map, the most complete cartographies of this area are those corresponding to the Quaternary deposits elaborated in the context of the Spanish Geological Map (Pedraza & Fernández, 1981), those of glacial morphology of the Gredos and Pinar gorges associated with chronological studies (Palacios et al, 2011(Palacios et al, , 2012 and that of the Gredos Cirque associated with a study on snow avalanches (Soteres, Pedraza, et al, 2020). Although all the data contained in these maps have been fundamental to carry out this work, it is the avalanche map that most coincides with the objectives presented in this work due to its methodology and technique of preparing the different maps.…”

Section: Previous Workmentioning

confidence: 99%

“…The majority are covered by unconsolidated debris of different grain size on their floors and occasionally they can contain glacial till and periglacial protalus or pronival ramparts. Some of these landforms have been identified as frequent snow avalanche paths in the area (Soteres, Pedraza, et al, 2020).…”

Section: Structural Landformsmentioning

confidence: 99%

Glacial geomorphology of the High Gredos Massif: Gredos and Pinar valleys (Iberian Central System, Spain)

et al. 2020

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We present a detailed geomorphological map of the landform assemblages originated by the two major paleoglaciers of the Sierra de Gredos mountain range in the Spanish Iberian Central System. Based on previous works, our map focused on the features formed by Gredos and Pinar paleoglaciers during the last glaciation and subsequent glacial events. Based on a remote sensing analysis and exhaustive field surveys, we identified with great accuracy the local distribution of glacial, periglacial, mass movement, structural, fluvial, and lacustrine features. We recognized three main glacial geomorphological formations representing: (i) the maximum glacial extension reached (peripheral deposits); (ii) the culmination of glacial conditions (principal moraines) and (iii) the local glacial withdrawal (internal deposits). Our map offers a renewed spatial framework on which to conduct higher-resolution glacial chronologies, especially of Late Glacial and Holocene glacial activity, providing key information for performing future paleoclimatic reconstructions of the northern hemisphere mid-latitudes.

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“…Consequently, the advancement of the novel methods for hazard assessment and risk management of snow avalanche has increasingly become popular around the world for effective mitigation and resilience [12]- [14]. Modeling the snow motion has shown to be essential for understanding the mountainous landscape dynamics to assess the avalanche hazard susceptibility [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

“…Susceptibility mapping techniques can complement early warning systems and temporal models to save lives [29]- [32]. Traditional techniques for susceptibility mapping limits to geospatial modeling using either simple statistical or decision analysis methods [15], [17], [27], [33]- [35]. Although the application of machine learning (ML) for susceptibility mapping of a wide range of natural hazards has been established, snow avalanche is yet to be explored [36].…”

Section: Introductionmentioning

confidence: 99%

Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

et al. 2020

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Snow avalanche as a natural disaster severely affects socio-economic and geomorphic processes through damaging ecosystems, vegetation, landscape, infrastructures, transportation networks, and human life. Modeling the snow avalanche has been seen as an essential approach for understanding the mountainous landscape dynamics to assess hazard susceptibility leading to effective mitigation and resilience. Therefore, the main aim of this study is to introduce and implement an ensemble machine learning model of random subspace (RS) based on a classifier, functional tree (FT), named RSFT model for snow avalanche susceptibility mapping at Karaj Watershed, Iran. According to the best knowledge of literature, the proposed model, RSFT, has not earlier been introduced and implemented for snow avalanche modeling and mapping over the world. Four benchmark models, including logistic regression (LR), logistic model tree (LMT), alternating decision tree (ADT), and functional trees (FT) models were used to check the goodnessof-fit and prediction accuracy of the proposed model. To achieve this objective, the most important factors among many climatic, topographic, lithologic, and hydrologic factors, which affect the snow accumulation and snow avalanche occurrence, were determined by the information gain ratio (IGR) technique. The goodness-of-fit and prediction accuracy of the models were evaluated by some statistical-based indexes including, sensitivity, specificity, accuracy, kappa, and area under the ROC curve, Friedman and Wilcoxon sign rank tests. Results indicated that the ensemble proposed model (RSFT), had the highest performance (Sensitivity = 94.1%, Specificity = 92.4%, Accuracy = 93.3%, and Kappa = 0.782) rather than the other soft-computing benchmark models. The snow avalanche susceptibility maps indicated that the high and very high susceptibility avalanche areas are located in the north and northeast parts of the study area, which have a higher elevation with more precipitation and lower temperatures. INDEX TERMSSnow avalanche, susceptibility mapping, ensemble approach, feature selection. AMIRHOSEIN MOSAVI graduated from Kingston University London, U.K. He received the Ph.D. degree in applied informatics. He is currently a Data Scientist in climate change, sustainability, and hazard prediction. He is a Senior Research Fellow with Oxford Brookes University. He is also an Alexander von Humboldt Research Fellow in big data, the IoT, and machine learning. He was a recipient of the Green-Talent Award, the UNESCO Young Scientist Award, the ERCIM Alain Bensoussan Fellowship Award, the Campus France Fellowship Award, the Campus Hungary Fellowship Award, and the Endeavour-Australia Leadership.

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Snow avalanche susceptibility of the Circo de Gredos (Iberian Central System, Spain)

Cited by 17 publications

References 28 publications

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Identification of Fuzzy Information in English Interpretation Based on the Digital Elevation Model

Glacial geomorphology of the High Gredos Massif: Gredos and Pinar valleys (Iberian Central System, Spain)

Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping

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