Pleistocene glacial relief of the central part of Mt. Prokletije (Albanian Alps)

The central and highest area of Mt. Prokletije (Albanian Alps) is situated in northern Albania and eastern Montenegro (at 42°30′N). The highest peak is Maja e Jezerces (2694 m). Detailed geomorphological mapping was used to reconstruct the positions of former glaciers. The longest Ropojana glacier had a length of 12.5 km and surface of 20 km²; others include Valbona Glacier (9.5 km, 10.5 km²), Grbaja Glacier (5 km, 6.7 km²) and Bogićevica Glacier (6 km, 6.9 km²). Three series of moraines can be distinguished: the lowest at an average altitude of 990 m (average ELA 1750 m), the middle series at 1350 m (ELA 1942 m), and the highest at 1900 m (ELA 2123 m). As no advanced dating methods have yet been used to provide a numerical chronological framework for these features, hypotheses are made based on the comparison with the advanced studies of other similar mountains in the Mediterranean region. The moraines of the first stage (lowest series) correspond to one of pre-LGM glaciations (Middle or even Early Würmian), the second stage moraines probably correspond to LGM, and the third stage could be attributed to Younger Dryas. The mapping included a number of inactive and active rock glaciers, as well as three small active glaciers (surface 5 ha and less), at 1980–2100 m altitude, in the area close to Maja e Jezerces.     

Spatial modelling of soil erosion potential in a mountainous watershed of South-eastern Serbia

This study employed genetic adaptive neural networks in the classification of high-resolution satellite images from which data related to surface conditions in mountainous areas of Taiwan were derived. Principal component analysis was then used to extract factors associated with the threat of natural disaster, and logistic regression was used to compute the probability of disaster occurrence. Through field surveys, interviews with district officials and a review of relevant literature, the probability of a sediment disaster was estimated as well as the vulnerability of the villages concerned and the degree to which these villages were prepared, to construct a risk evaluation model. A geographic information system was used to plot regional risk maps as a means to enhance the safety of residents in the study area. The risk assessment model can be used by authorities to make provisions for high-risk areas, to reduce the number of casualties and social costs of sediment disasters.     

Occurrence of Sn-Bearing colusite in the ore-body “T” of the Bor copper deposit, Serbia

The ore body “T” is the newly discovered massive-pyrite type one which is located in the central part of the Bor copper mine. The main copper minerals are chalcocite-digenite, covellite and enargite. Small amounts of colusite are frequently present in the ore-body. It mostly occurs as the distinct exsolutions in digenite and, associating with enargite and covellite. Composition of the studied colusite shows enriched Sn content, giving an empirical formula from Cu_24.7V_1.8Fe_0.2As_5.1Sb_0.2Sn_0.8S₃₂ to Cu_26.7V_2.0Fe_0.3A_s3.0Sb_0.3Sn_3.5S₃₂. This colusite represents a solid solution between colusite and nekrasovite within a range of 14–54 mol % nekrasovite. Most of the analyses show content of <50 mol % nekrasovite corresponding to the Sn-bearing colusite variety, while one analysis shows content of 54 mol % nekrasovite corresponding to the As-bearing nekrasovite.     

Ophiolitic detritus in Cretaceous clastic formations of the Dinarides (NW Croatia): evidence from Cr-spinel chemistry

Cr-spinel is a common heavy mineral in the sandstones of Cretaceous synorogenic sedimentary formations of the NW Dinarides, Croatia. The rocks occur in isolated exposures in the uplifted basement units of Medvednica, Ivanščica, Žumberak and Samobor Mountains near Zagreb. In this area, evidence of the early Alpine evolution of the Dinarides is obscured due to strong dismemberment of pre-Tertiary tectonostratigraphic units resulting from an intense tectonic history, as well as due to the widespread sedimentary cover of the Pannonian Basin. Electron microprobe analyses of detrital Cr-spinels from the Oštrc Formation reveal that in the Early Cretaceous the ophiolitic source area was predominantly composed of harzburgite peridotites and associated cumulate rocks, which developed in a supra-subduction zone setting. The supply of Cr-spinels with the same chemical signature remained dominant until the end of the Cretaceous, suggesting that exposed remnants of the same ophiolite belt persisted through the Cretaceous and/or that recycling was significant. Similarities with data reported from the Northern Calcareous Alps and the Transdanubian Central Range imply that a rather extensive harzburgitic ophiolite belt probably extended along the Adriatic margin during the Early Cretaceous. A slight trend of increasing variation in the Cr# is observed from the Early to the latest Cretaceous, suggesting that the source areas became more heterogeneous with the ongoing Cretaceous tectonic evolution. Differences in Cr-spinel compositions in two contemporaneous latest Cretaceous formations are well in line with existing data on heavy mineral proportions, which together identify contrasting hinterland geology for these formations and strongly suggest the coeval existence of two separate basins. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).     

An investigation of wind-driven topographically controlled motions in the Northern Adriatic

The present paper describes a three-dimensional hydrodynamical numerical model of the Northern Adriatic. The model is based on the approach of N.S. Heaps in which the integral transformations are used to reproduce the vertical distribution of velocity. The model is applied to reproduce the wind-induced motion in the Northern Adriatic during winter. Hydrographic, sea level and current data collected during the MEDALPEX are used to verify the model predictions. Analysis of the empirical data suggests that the bura wind induces the most pronounced, although transient, contribution to the Northern Adriatic current field. The model predictions clearly show the controlling influence of a shallower bottom along the Italian coast. The model to data comparison suggests for the eddy viscosity coefficient value an order of magnitude lower than expected from literature data. The quadratic law for bottom friction and wind-stress curl have been identified as possible improvements of the model.     

Meteoric phreatic speleothems and the development of cave stratigraphy: An example from Tounj Cave, Dinarides, Croatia

Speleothems occurring in some caves of the carbonate Dinarides line all channel surfaces, and have been deposited from meteoric waters under phreatic conditions. Such phreatic speleothemic deposition modifies common experience (l) that meteoric phreatic conditions cause dissolutional widening of cave voids, and (2) that speleothems imply vadose conditions. The phreatic speleothems described here postdate an early polygenetic evolution of the cave voids, and predate the last, vadose stage. They were likely produced during the late/postglacial warming period, when dissolved carbonate was amply supplied, and when there was much water available for saturation of underground voids. Phreatic speleothems may be used as a tool for time correlation of internal deposits, both within one cave and within a karst region. They indicate an important stage in the history of the ground-water regime of an area. In general, phreatic speleothems help in better understanding of the development of subterranean voids and related karst/palaeokarst.     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.