Examination of physical processes of convective cell evolved from a MCS — Using a different model initialization

The present study is focused on examination of the physical processes of convective cell evolved from a MCS occurred on 4 November 2011 over Genoa, Italy. The Quantitative Precipitation Forecasts (QPF) have been performed using WRF v3.6 model under different configurations and cloud permitting simulations. The results indicate underestimation of the amount of precipitation and spatial displacement of the area with a peak 24-h accumulated rainfall in (mm). Our main objective in the research is to test the cloud model ability and performance in simulation of this particular case. For that purpose a set of sensitivity experiments under different model initializations and initial data have been conducted. The results also indicate that the merging process apparently alters the physical processes through low- and middle-level forcing, increasing cloud depth, and enhancing convection. The examination of the microphysical process simulated by the model indicates that dominant production terms are the accretion of rain by graupel and snow, probabilistic freezing of rain to form graupel and dry and wet growth of graupel. Experiment under WRF v3.6 model initialization has shown some advantage in simulation of the physical processes responsible for production and initiation of heavy rainfall compared to other model runs. Most of the precipitation came from ice-phase particles-via accretion processes and the graupel melting at temperature T₀ ≥ 0°C. The rainfall intensity and accumulated rainfall calculated by the model closely reflect the amount of rainfall recorded. Thus, the main benefit is to better resolve convective showers or storms which, in extreme cases, can give rise to major flooding events. In such a way, this model may become major contributor to improvements in weather analysis and small-scale atmospheric predictions and early warnings of such subscale processes.     

Modeling Urban Land Use Changes Using Support Vector Machines

Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.     

Stratigraphic and lithologic characteristics of Pleistocene fluvial deposits in the Danube and Sava riparian area near Belgrade (Serbia)

The Quaternary sediments in the Danube and Sava riparian area near Belgrade have a considerable thickness. Several categories of deposits (fluvial-lacustrine, fluvial and aeolian) of Pliocene and Quaternary age have been identified. Their thickness, granulometric composition and paleontological features change depending on the distance from the recent Danube and Sava riverbeds. The Pleistocene fluvial deposits are underlain by sediments of the Late Miocene (Sarmatian and Pannonian) or the Plio-Pleistocene age, and are overlain by fluvial-palustrine deposits of the Pleistocene age and recent alluvial deposits. Pleistocene fluvial deposits that form a major part of the Quaternary sediments, have a great significance, since they are proved to be excellent collectors of ground water. Although these deposits are at lower altitudes in the area of Srem, they could be correlated with the high Danube and Morava terraces in Serbia and Drava in Croatia on the basis of their lithologic and paleontological features.     

Integration of GIS and video surveillance

This paper discusses the integration of three-dimensional (3D) geographic information systems (GIS) and video surveillance systems using augmented reality (AR) techniques. The motivation for this integration is to overcome problems faced by conventional video surveillance systems. Explicit information concerning which camera currently monitors what area in such systems is missing; therefore, insight into the situation depends heavily on the operator’s training and experience. To ensure the complete coordination and monitoring of a situation in a system with multiple cameras, it is necessary to introduce a single reference system. GIS arises as a natural solution because it not only provides a solid ground truth but also provides semantic information that can be highly important in certain video surveillance applications. To integrate information into a GIS application, that information must be georeferenced. Based on our previous research regarding the addition of georeferencing information to surveillance video, this paper introduces models that can be applied to help integrate video and GIS. With an analogy to Milgram’s continuum between the real world and virtual reality, and analogous to the augmented reality and augmented virtuality in Milgram’s continuum, two models of integration are defined here: GIS-augmented video and video-augmented GIS. Then, we define the architecture of GIS-based video surveillance based on these proposed integration models, and finally, a prototype is implemented. The implemented prototype serves as a basis for analysing possible applications of real-world systems based on the integration of GIS and video.     

Removal of heavy metal ions from drinking water by alginate-immobilised <Emphasis Type="Italic">Chlorella sorokiniana</Emphasis>

This paper investigates the potential of alginate-immobilised Chlorella sorokiniana for removing Cu²⁺, Ni²⁺ and Cd²⁺ ions from drinking water solutions. The effects of initial metal concentrations, contact times and temperatures on the biosorptions and removal efficiencies of the tested metals were investigated at initial pH values of 5, and pH effects were studied within the range of 3–7. When studying the effects of initial metal concentrations, the highest experimental removal yields achieved for Cu²⁺, Ni²⁺ and Cd²⁺ ions were 97.10, 50.94 and 64.61 %, respectively. The maximum biosorption capacities obtained by the Langmuir isotherm model for the biosorptions of Cu²⁺, Ni²⁺ and Cd²⁺ ions by alginate-immobilised C. sorokiniana were found to be 179.90, 86.49 and 164.50 mg/g biosorbent, respectively. The experimental data followed pseudo-second-order kinetics. At an initial metal concentration of 25 mg/L, immobilised algae could be used in at least 5 successive biosorption–desorption cycles. SEM and EDS analyses revealed that the metals bonded to the biosorbent. Bi- and multi-metal systems of Cu²⁺, Ni²⁺ and Cd²⁺ were investigated at initial metal concentrations of 30, 50 and 100 mg/L. The removal of Cd²⁺ as well as Ni²⁺ in such systems was negatively affected by the presence of Cu²⁺. The removal efficiency for Cu²⁺ in multi-metal systems decreased by 5–7 %, whilst in the cases of Cd²⁺ and Ni²⁺ the efficiencies decreased by up to 30 %. Nevertheless, the results obtained show that alginate-immobilised C. sorokiniana can efficiently remove the metals tested from polluted drinking water sources.