Works matching IS 0921030X AND DT 2022 AND VI 113 AND IP 2
Results: 25
Local tsunamigenic sources in Greece, identified by pattern recognition.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1335, doi. 10.1007/s11069-022-05349-0
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Experimental investigation on anti-sliding performance of grouted micro-pipe pile groups.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1367, doi. 10.1007/s11069-022-05351-6
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Gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1349, doi. 10.1007/s11069-022-05350-7
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Numerical modeling and hazard implications of landslides at the Ardillas Volcanic Dome (Tacaná Volcanic Complex, Mexico-Guatemala).
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- Natural Hazards, 2022, v. 113, n. 2, p. 1305, doi. 10.1007/s11069-022-05348-1
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Flood risk mapping for the lower Narmada basin in India: a machine learning and IoT-based framework.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1285, doi. 10.1007/s11069-022-05347-2
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Multivariate fire risk models using copula regression in Kalimantan, Indonesia.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1263, doi. 10.1007/s11069-022-05346-3
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Application of the self-calibrated palmer drought severity index and standardized precipitation index for estimation of drought impact on maize grain yield in Pannonian part of Croatia.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1237, doi. 10.1007/s11069-022-05345-4
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GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin, North Sikkim.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1199, doi. 10.1007/s11069-022-05344-5
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Identifying critical combination of roadside slopes susceptible to rainfall-induced failures.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1177, doi. 10.1007/s11069-022-05343-6
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Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1151, doi. 10.1007/s11069-022-05341-8
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Genesis and simultaneous occurrences of the super cyclone Kyarr and extremely severe cyclone Maha in the Arabian Sea in October 2019.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1133, doi. 10.1007/s11069-022-05340-9
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Comparative review of data-driven landslide susceptibility models: case study in the Eastern Andes mountain range of Colombia.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1105, doi. 10.1007/s11069-022-05339-2
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Landslide susceptibility evaluation in the Chemoga watershed, upper Blue Nile, Ethiopia.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1391, doi. 10.1007/s11069-022-05338-3
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Hybrid stochastic-mechanical modeling of precipitation thresholds of shallow landslide initiation.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1083, doi. 10.1007/s11069-022-05337-4
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Flood vulnerability and buildings' flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1043, doi. 10.1007/s11069-022-05336-5
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Short communication: predicting typhoon tracks around Korea.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1385, doi. 10.1007/s11069-022-05335-6
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An assessment of Dungale landslide using remotely piloted aircraft system (RPAS), ground penetration radar (GPR), and Slide & RS<sup>2</sup> Softwares.
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- Natural Hazards, 2022, v. 113, n. 2, p. 1017, doi. 10.1007/s11069-022-05334-7
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Spatial and temporal evolution characteristics and causes of drought and flood in the Henan section of the Yellow River.
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- Natural Hazards, 2022, v. 113, n. 2, p. 997, doi. 10.1007/s11069-022-05333-8
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Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China.
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- Natural Hazards, 2022, v. 113, n. 2, p. 965, doi. 10.1007/s11069-022-05332-9
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Liquefaction resistance evaluation of soils using artificial neural network for Dhaka City, Bangladesh.
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- Natural Hazards, 2022, v. 113, n. 2, p. 933, doi. 10.1007/s11069-022-05331-w
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Incorporating shear stiffness into post-fire debris flow statistical triggering models.
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- Natural Hazards, 2022, v. 113, n. 2, p. 913, doi. 10.1007/s11069-022-05330-x
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Discussion on the tree-based machine learning model in the study of landslide susceptibility.
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- Natural Hazards, 2022, v. 113, n. 2, p. 887, doi. 10.1007/s11069-022-05329-4
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A conceptual framework for integrated management of disasters recovery projects.
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- Natural Hazards, 2022, v. 113, n. 2, p. 859, doi. 10.1007/s11069-022-05328-5
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Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility.
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- Natural Hazards, 2022, v. 113, n. 2, p. 833, doi. 10.1007/s11069-022-05326-7
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Spatial prediction of highway slope disasters based on convolution neural networks.
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- Natural Hazards, 2022, v. 113, n. 2, p. 813, doi. 10.1007/s11069-022-05325-8
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