| 1. |
- Antić, Aleksandar, et al.
(författare)
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Loess and geotourism potential of the Braničevo District (NE Serbia) : From overexploitation to paleoclimate interpretation
- 2023
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Ingår i: Open Geosciences. - 2391-5447. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- The use of loess as a resource for paleoclimatic research is quite well established. In Serbia, a significant number of loess sequences have been preserved in old brickyards. The results of the previously conducted research indicate extremely valuable data that enable a better understanding of the mid- to late Pleistocene climatic evolution in this part of Europe, as well as human dispersal from Africa to Europe via the so-called Danubian migration corridor. The aim of this study is to evaluate the geotourism potentials of the loess profiles in Pożarevac (northeastern Serbia). The goal is to determine their geotourism potential for paleoclimate interpretation. The Modified Geoheritage Assessment Model method has identified exceptional geotourism potentials that can be implemented in the tourism market. Paleoclimatic data can serve as indicators for the development of scientific visitor centers for the promotion and popularization of paleoclimate science and museums, which will affirm sustainable socio-economic development through multidisciplinary interpretation. By combining geological, paleoclimatic, archaeological, biological, and other values that reveal natural and anthropogenic events from the distant past, it is possible to create a very competitive geotourism destination, whose sustainability can be passed on to future generations.
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| 2. |
- Backman, Jan, 1948-, et al.
(författare)
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Expanding the Cenozoic paleoceanographic record in the central Arctic Ocean: IODP Expedition 302 synthesis
- 2009
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Ingår i: Central European Journal of Geosciences. - : Walter de Gruyter GmbH. - 1896-1517. ; 1:2, s. 157-175
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Tidskriftsartikel (refereegranskat)abstract
- The Arctic Coring Expedition (ACEX) proved to be one of the most transformational missions in almost 40 year of scientific ocean drilling. ACEX recovered the first Cenozoic sedimentary sequence from the Arctic Ocean and extended earlier piston core records from ≈1.5 Ma back to ≈56 Ma. The results have had a major impact in paleoceanography even though the recovered sediments represents only 29% of Cenozoic time. The missing time intervals were primarily the result of two unexpected hiatuses. This important Cenozoic paleoceanographic record was reconstructed from a total of 339 m sediments. The wide range of analyses conducted on the recovered material, along with studies that integrated regional tectonics and geophysical data, produced surprising results including high Arctic Ocean surface water temperatures and a hydrologically active climate during the Paleocene Eocene Thermal Maximum (PETM), the occurrence of a fresher water Arctic in the Eocene, ice-rafted debris as old as middle Eocene, a middle Eocene environment rife with organic carbon, and ventilation of the Arctic Ocean to the North Atlantic through the Fram Strait near the early-middle Miocene boundary. Taken together, these results have transformed our view of the Cenozoic Arctic Ocean and its role in the Earth climate system.
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| 3. |
- Saberi, Azim, et al.
(författare)
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Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm : A case study (Khuzestan Province, Iran)
- 2023
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Ingår i: Open Geosciences. - : De Gruyter Open. - 2391-5447. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.
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| 4. |
- Sarfraz, Yasir, et al.
(författare)
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Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
- 2022
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Ingår i: Open Geosciences. - : De Gruyter Open Ltd. - 2391-5447. ; 14:1, s. 1606-1635
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Tidskriftsartikel (refereegranskat)abstract
- Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, andmachine learning (ML), along themain road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performancematrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelumroad is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslideprone zones along road corridors.
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