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.     

Effects of demand estimates on the evaluation and optimality of service centre locations

Public service systems, such as emergency health care, police or fire brigades, are critical for day-to-day functioning of the society. To design and operate these systems efficiently much data needs to be collected and properly utilised. Here, we use the OpenStreetMap (OSM) data to model the demand points (DPs), which approximate the geographical location of customers, and the road network, which is used to access or distribute services. We consider all inhabitants as customers, and therefore to estimate the demand, we use the available population grids. People are changing their location in the course of the day and thus the demand for services is changing accordingly. In this paper, we investigate how the used demand estimate affects the optimal design of a public service system. We calculate and compare efficient designs corresponding to two demand models, a night-time demand model when the majority of inhabitants rest at home and the demand model derived from the 24-hour average of the population density. We propose a simple measure to quantify the differences between population grids and we estimate how the size of differences affects the optimal structure of a public service system. Our analyses reveal that the efficiency of the service system is not only dependent on the placement strategy, but an inappropriate demand model has significant effects when designing a system as well as when evaluating its efficiency.     

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.     

Appraisal of changes in sewage sludge management

A positive energy balance caused by accelerated production of CH₄ and additional financial benefits associated with shorter retention times, reduction in volumes, easier dewatering, enhanced hygiene, etc., are the reasons why most modern wastewater treatment plants are newly equipped with hydrothermal pretreatment units. This study examined how the changes in composition of the pretreated sewage sludge fermentation residues manifested themselves in subsequent processing by pyrolysis, both in terms of technology and financial impacts. It has been found that concentrations of heavy metals and other hazardous inhibitors might increase; however, the overall biotoxicity is lower due to their immobilization in the charred mass. Moreover, charring of the fermentation residues results in significant financial as well as technological benefits.     

Enhancement of a hypoplastic model for granular soil–structure interface behaviour

Modelling of interfaces in geotechnical engineering is an important issue. Interfaces between structural elements (e.g., anchors, piles, tunnel linings) and soils are widely used in geotechnical engineering. The objective of this article is to propose an enhanced hypoplastic interface model that incorporates the in-plane stresses at the interface. To this aim, we develop a general approach to convert the existing hypoplastic model with a predefined limit state surface for sands into an interface model. This is achieved by adopting reduced stress and stretching vectors and redefining tensorial operations which can be used in the existing continuum model with few modifications. The enhanced interface model and the previous model are compared under constant-load, stiffness and volume conditions. The comparison is followed by a verification of two the approaches for modelling the different surface roughness. Subsequently, a validation between available experimental data from the literature versus simulations is presented. The new enhanced model gives improved predictions by the incorporation of in-plane stresses into the model formulation.