Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s08.17
A. F. Jocea, Liviu Porumb, Lucian Necula, Alexandru-Cosmin Grivei, D. Raducanu
The current information age has led, globally, to an exponential increase regarding the availability and the use of the information, both structured and unstructured, a phenomenon known as Big Data. The term Big Data refers not only to the massive volume and variety of data itself, but also to the set of technologies that surround it, in order to collect, store, retrieve, manage, process and analyze data in order to solve complex problems in society, respectively for increasing the quality of life in all its aspects. Given that approximately 80% of the data generated daily has a spatial component, and studies indicate that more than 150 zettabytes (150 trillion gigabytes) of data will require analysis by 2025, it is necessary to create Big Data solutions for storage, organizing, manipulating, viewing, and retrieving relevant information. Today, in the midst of the �data revolution�, more and more countries are launching ambitious programs aimed at developing their use. These programs test the ability of decisionmakers to recognize, structure and exploit data, which is considered a valuable resource, and create the means to generate value from it by facilitating access. The Big Data phenomenon has also conquered the military field, in which the current and emerging object of large-scale data analysis areas is the exploitation of classical techniques such as rule-based systems, shape analysis, tree structures and other analysis technologies in order to develop efficient tools. In this paper we will start from the investigation of the basic characteristics of Big Data and continued with technical details that involves the generation, collection, storage and analysis of geospatial Big Data needed to transform these data into an actionable intelligence.
{"title":"GENERATING ACTIONABLE INTELLIGENCE FROM GEOSPATIAL BIG DATA: STATE OF THE ART AND CONCEPT","authors":"A. F. Jocea, Liviu Porumb, Lucian Necula, Alexandru-Cosmin Grivei, D. Raducanu","doi":"10.5593/sgem2022/2.1/s08.17","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s08.17","url":null,"abstract":"The current information age has led, globally, to an exponential increase regarding the availability and the use of the information, both structured and unstructured, a phenomenon known as Big Data. The term Big Data refers not only to the massive volume and variety of data itself, but also to the set of technologies that surround it, in order to collect, store, retrieve, manage, process and analyze data in order to solve complex problems in society, respectively for increasing the quality of life in all its aspects. Given that approximately 80% of the data generated daily has a spatial component, and studies indicate that more than 150 zettabytes (150 trillion gigabytes) of data will require analysis by 2025, it is necessary to create Big Data solutions for storage, organizing, manipulating, viewing, and retrieving relevant information. Today, in the midst of the �data revolution�, more and more countries are launching ambitious programs aimed at developing their use. These programs test the ability of decisionmakers to recognize, structure and exploit data, which is considered a valuable resource, and create the means to generate value from it by facilitating access. The Big Data phenomenon has also conquered the military field, in which the current and emerging object of large-scale data analysis areas is the exploitation of classical techniques such as rule-based systems, shape analysis, tree structures and other analysis technologies in order to develop efficient tools. In this paper we will start from the investigation of the basic characteristics of Big Data and continued with technical details that involves the generation, collection, storage and analysis of geospatial Big Data needed to transform these data into an actionable intelligence.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116569015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s10.40
Joachim Vercruysse, G. Deruyter
As a consequence of climate change, in some regions, more intense rain showers go hand in hand with longer dry periods. The subsequent more and more severe droughts can have devastating effects on many economic and social sectors. Therefore, it is necessary to be able to predict and assess the consequences of these droughts on a local scale, in order to develop policies to cope. Drought assessment needs a lot of detailed and accurate input-data, such as land use, land cover, soil moisture, vegetation, evapotranspiration, etc., often obtained by continuous earth monitoring by satellites. Satellite images are generally converted into indices, of which the Normalized Difference Vegetation Index (NDVI) is one of the most widely used. It was developed for use with Landsat imagery and allows for the classification of satellite images for land use and the assessment of the vegetation�s vitality. In this research, a new composite index is presented and compared to the NDVI to be used with Sentinel-2A imagery, having higher resolution and more spectral bands than Landsat. This new composite index can be used to detect water and vegetation. Test results show that this newly developed composite index achieves a better accuracy through Support Vector Machine (SVM) classification than the widely used NDVI. Although further validation is necessary, the results promise a possible amelioration of vegetation related input data for drought assessment and management.
{"title":"OPTIMISING VEGETATION-INPUT FOR DROUGHT ASSESSMENT WITH SENTINEL-2A DATA","authors":"Joachim Vercruysse, G. Deruyter","doi":"10.5593/sgem2022/2.1/s10.40","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s10.40","url":null,"abstract":"As a consequence of climate change, in some regions, more intense rain showers go hand in hand with longer dry periods. The subsequent more and more severe droughts can have devastating effects on many economic and social sectors. Therefore, it is necessary to be able to predict and assess the consequences of these droughts on a local scale, in order to develop policies to cope. \u0000Drought assessment needs a lot of detailed and accurate input-data, such as land use, land cover, soil moisture, vegetation, evapotranspiration, etc., often obtained by continuous earth monitoring by satellites. Satellite images are generally converted into indices, of which the Normalized Difference Vegetation Index (NDVI) is one of the most widely used. It was developed for use with Landsat imagery and allows for the classification of satellite images for land use and the assessment of the vegetation�s vitality. \u0000In this research, a new composite index is presented and compared to the NDVI to be used with Sentinel-2A imagery, having higher resolution and more spectral bands than Landsat. This new composite index can be used to detect water and vegetation. \u0000Test results show that this newly developed composite index achieves a better accuracy through Support Vector Machine (SVM) classification than the widely used NDVI. Although further validation is necessary, the results promise a possible amelioration of vegetation related input data for drought assessment and management.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s10.34
H. Ștefănie, A. Radovici, Alexandru Mereuță, Horia Cămărășan, N. Ajtai
It is well known that atmospheric aerosols have both a direct and an indirect impact on the Earth's systems and have natural or anthropogenic origins. In this paper we present the results of the 2022 spring-time lidar measurements conducted within the European Aerosol Research Lidar Network (EARLINET) using a multi-wavelength Raman and depolarization LIDAR system operated in Cluj-Napoca, Romania. The Cluj-Napoca lidar system (CLOP) emission is based on a Nd-YAG laser Continuum INLITE II-30, which has a repetition rate of 30 Hz. The radiation at 1064, 532, and 355 nm is simultaneously emitted into atmosphere. The backscattered radiation is collected by a Cassegrain type telescope with a focal length of 1500 mm. The signal detection unit has a total of 6 detection channels, 4 channels for the elastically backscattered radiation at 1064, 532 (cross and parallel), and 355 nm and 2 channels for the Raman radiation backscattered by nitrogen molecules at 607 and 387 nm. This type of system meets the requirements of the EARLINET network and is suitable to provide reliable data on optical parameters for aerosol characterization. The reference period for the observations was in accordance with EARLINET�s fixed measurements schedule. The preliminary results indicated the presence of both natural (mineral dust) and anthropogenic (resulted from seasonal biomass burning) aerosols originated from local sources or subject of long-range transport (LRT).
{"title":"DETECTION OF LOCAL AND LONG-RANGE TRANSPORTED AEROSOL INTRUSIONS OVER CLUJ-NAPOCA, ROMANIA USING MULTIWAVELENGTH LIDAR MEASUREMENTS IN SPRING 2022","authors":"H. Ștefănie, A. Radovici, Alexandru Mereuță, Horia Cămărășan, N. Ajtai","doi":"10.5593/sgem2022/2.1/s10.34","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s10.34","url":null,"abstract":"It is well known that atmospheric aerosols have both a direct and an indirect impact on the Earth's systems and have natural or anthropogenic origins. In this paper we present the results of the 2022 spring-time lidar measurements conducted within the European Aerosol Research Lidar Network (EARLINET) using a multi-wavelength Raman and depolarization LIDAR system operated in Cluj-Napoca, Romania. The Cluj-Napoca lidar system (CLOP) emission is based on a Nd-YAG laser Continuum INLITE II-30, which has a repetition rate of 30 Hz. The radiation at 1064, 532, and 355 nm is simultaneously emitted into atmosphere. The backscattered radiation is collected by a Cassegrain type telescope with a focal length of 1500 mm. The signal detection unit has a total of 6 detection channels, 4 channels for the elastically backscattered radiation at 1064, 532 (cross and parallel), and 355 nm and 2 channels for the Raman radiation backscattered by nitrogen molecules at 607 and 387 nm. This type of system meets the requirements of the EARLINET network and is suitable to provide reliable data on optical parameters for aerosol characterization. The reference period for the observations was in accordance with EARLINET�s fixed measurements schedule. The preliminary results indicated the presence of both natural (mineral dust) and anthropogenic (resulted from seasonal biomass burning) aerosols originated from local sources or subject of long-range transport (LRT).","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127074010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s11.47
Aleksandra Ravnachka, V. Stoyanova
The current research aims to apply cluster analysis using the software ArcGIS in the study of the food industry in Bulgaria for the period 2010 to 2020. The use of clustering methods is necessary to differentiate homogeneous groups of administrative-territorial units of NUTS 3 level on certain indicators to reveal several features and implement specific economic policies and measures for areas of a cluster and others. The grouping of the areas according to the considered indicators was done with the tool Grouping Analysis. Grouping and classification techniques are some of the most widely used methods in machine learning. We have selected No_spatial_constraint for the Spatial Constraints parameter, for grouping using the K-Means algorithm. Based on the results of the �average intergroup connection� method, the areas are grouped into 7 clusters (food industry, 2010 and 2020; food and beverage products for the period 2010-2020) and into 4 clusters (tobacco production for the period 2010-2020). The selection of indicators based on which the clusters are formed is following the generally accepted indicators for assessing the state and importance of the food industry in the structure of the economy and their information accessibility. The following indicators were used output for 2010 and 2020, employees for 2010 and 2020, and export earnings for 2010 and 2020 for the given territorial unit The territorial distribution of the population, in combination with the historical and modern economic development of the settlements, forms the regional differences in the development of the food industry in the country. The cluster analysis of certain indicators for the assessment of the food industry at the NUTS 3 level for 2010 and 2020 shows some change in the trends in the territorial development of the industry. The cluster analysis shows that there are slight territorial differences at the NUTS 3 level in food production, with large consumer centers and markets being the most important. In the activities of tobacco and beverage production, the territorial differences are minimal.
{"title":"GIS-AIDED GEOSPATIAL ANALYSIS OF THE FOOD INDUSTRY OF BULGARIA (2010-2020)","authors":"Aleksandra Ravnachka, V. Stoyanova","doi":"10.5593/sgem2022/2.1/s11.47","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s11.47","url":null,"abstract":"The current research aims to apply cluster analysis using the software ArcGIS in the study of the food industry in Bulgaria for the period 2010 to 2020. The use of clustering methods is necessary to differentiate homogeneous groups of administrative-territorial units of NUTS 3 level on certain indicators to reveal several features and implement specific economic policies and measures for areas of a cluster and others. The grouping of the areas according to the considered indicators was done with the tool Grouping Analysis. Grouping and classification techniques are some of the most widely used methods in machine learning. We have selected No_spatial_constraint for the Spatial Constraints parameter, for grouping using the K-Means algorithm. Based on the results of the �average intergroup connection� method, the areas are grouped into 7 clusters (food industry, 2010 and 2020; food and beverage products for the period 2010-2020) and into 4 clusters (tobacco production for the period 2010-2020). The selection of indicators based on which the clusters are formed is following the generally accepted indicators for assessing the state and importance of the food industry in the structure of the economy and their information accessibility. The following indicators were used output for 2010 and 2020, employees for 2010 and 2020, and export earnings for 2010 and 2020 for the given territorial unit The territorial distribution of the population, in combination with the historical and modern economic development of the settlements, forms the regional differences in the development of the food industry in the country. The cluster analysis of certain indicators for the assessment of the food industry at the NUTS 3 level for 2010 and 2020 shows some change in the trends in the territorial development of the industry. The cluster analysis shows that there are slight territorial differences at the NUTS 3 level in food production, with large consumer centers and markets being the most important. In the activities of tobacco and beverage production, the territorial differences are minimal.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133011560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s10.33
Michal Prochazka, Robert Pinkas, M. Janků, J. Stryk, J. Grosek
Road managers are obliged by law to regularly monitor the condition of road pavements as part of road inspections. Visual inspections provide basic information on the condition of the road and regular assessments are the basis for planning maintenance and repairs. These inspections are usually carried out from a dedicated car and recorded manually by an operator or done by special sophisticated and very costly devices with cameras and various sensors. Inspections are done in defined periods based on road class and type of inspection. This paper presents a pilot test of a new method of monitoring pavement defects based on visual inspection by an autonomous vehicle-mounted system with automatic real-time evaluation performed by this device. The device processes the video recordings and uses deep neural networks for the detection and classification of pavement defects. The resulting metadata and location are immediately sent from this device to the cloud infrastructure. All the data are GDPR safe by design, no images or videos leave the device. The detection is not meant to be as precise as detection made by special diagnostic cars, it is used to do instant community-based monitoring of significant damages on the road network and hence serves as a pre-selection tool to provide road administrators valuable data on where detailed inspection or diagnostics is needed. In addition to the pavement condition, other parameters related to road objects and equipment can also be evaluated.
{"title":"AUTOMATED DETECTION OF PAVEMENT DEFECTS USING COMPUTER VISION","authors":"Michal Prochazka, Robert Pinkas, M. Janků, J. Stryk, J. Grosek","doi":"10.5593/sgem2022/2.1/s10.33","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s10.33","url":null,"abstract":"Road managers are obliged by law to regularly monitor the condition of road pavements as part of road inspections. Visual inspections provide basic information on the condition of the road and regular assessments are the basis for planning maintenance and repairs. These inspections are usually carried out from a dedicated car and recorded manually by an operator or done by special sophisticated and very costly devices with cameras and various sensors. Inspections are done in defined periods based on road class and type of inspection. This paper presents a pilot test of a new method of monitoring pavement defects based on visual inspection by an autonomous vehicle-mounted system with automatic real-time evaluation performed by this device. The device processes the video recordings and uses deep neural networks for the detection and classification of pavement defects. The resulting metadata and location are immediately sent from this device to the cloud infrastructure. All the data are GDPR safe by design, no images or videos leave the device. The detection is not meant to be as precise as detection made by special diagnostic cars, it is used to do instant community-based monitoring of significant damages on the road network and hence serves as a pre-selection tool to provide road administrators valuable data on where detailed inspection or diagnostics is needed. In addition to the pavement condition, other parameters related to road objects and equipment can also be evaluated.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123338238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s10.35
O. Trofymchuk, Vyacheslav Vishnyakov, N. Sheviakina, V. Klymenko, O. Tomchenko
Wildfires are a problem all over the world. Plant and animal life are under threat of destruction. But this issue is especially relevant for Ukraine, where fires appear en masse throughout the country not only from weather factors but also from enemy shelling by the Russian army. In addition, there is the uncontrolled spread of fire to large areas of Ukraine due to limited access to emergency services. Because of hostilities, landmines, and the occupation of Ukrainian territory, Ukrainian rescuers and foresters are not always able to fully prevent and fight fires. To identify the facts of fires, and to determine the coordinates of fires that lead to large-scale fires in the specified conditions, space monitoring by methods of remote sensing of the Earth is effective. Using remote sensing data, the authors present a methodology for identifying thermal anomalies, the scale of fires, tracing the direction of their spread, etc. With the help of satellite monitoring are determined fires, and the causes of their occurrence makes it possible to predict further spread and assess the affected areas. The publication presents the results of research on the use of remote sensing data on determining the thermal anomalies, hotbeds, and fires, proposed approach to monitoring of natural areas of Ukraine. The temperature activity in and near the Black Sea Biosphere Reserve was determined.
{"title":"DETECTION OF THERMAL ANOMALIES AS A RESULT OF MILITARY ACTIONS IN UKRAINE BY REMOTE SENSING METHODS","authors":"O. Trofymchuk, Vyacheslav Vishnyakov, N. Sheviakina, V. Klymenko, O. Tomchenko","doi":"10.5593/sgem2022/2.1/s10.35","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s10.35","url":null,"abstract":"Wildfires are a problem all over the world. Plant and animal life are under threat of destruction. But this issue is especially relevant for Ukraine, where fires appear en masse throughout the country not only from weather factors but also from enemy shelling by the Russian army. In addition, there is the uncontrolled spread of fire to large areas of Ukraine due to limited access to emergency services. Because of hostilities, landmines, and the occupation of Ukrainian territory, Ukrainian rescuers and foresters are not always able to fully prevent and fight fires. To identify the facts of fires, and to determine the coordinates of fires that lead to large-scale fires in the specified conditions, space monitoring by methods of remote sensing of the Earth is effective. \u0000Using remote sensing data, the authors present a methodology for identifying thermal anomalies, the scale of fires, tracing the direction of their spread, etc. With the help of satellite monitoring are determined fires, and the causes of their occurrence makes it possible to predict further spread and assess the affected areas. The publication presents the results of research on the use of remote sensing data on determining the thermal anomalies, hotbeds, and fires, proposed approach to monitoring of natural areas of Ukraine. The temperature activity in and near the Black Sea Biosphere Reserve was determined.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116616800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s07.04
Daina Vasilevska, B. Rivza
In the practice of leading countries, digital transformation implies the industry principle of supporting the introduction of new technologies. At the same time, programs and projects can be built both on the principle of introducing any one promising group of technologies in several industries, where it is especially relevant, and provide for the digital transformation of a particular industry through the forced introduction of many technologically heterogeneous solutions that are in demand in a given sector of the economy or social sphere. Digital transformation contributes to solving systemic problems in industries, reorganizing labor and automating routine tasks. Thus, in the electric power industry, the number of accidents at infrastructure facilities is reduced, in construction - the level of injuries at the construction site. Digital products improve coordination within and between organizations and reduce transaction costs. For example, platform solutions in logistics reduce the role of intermediaries, while expanding the possibilities for communication with end users. The digital transformation of agriculture is largely based on the integrated implementation of a number of digital technologies within the interrelated concepts of precision farming and smart agriculture. The purpose of this study is to determine the level of digitalization of agriculture in Latvia. Determine which digital technologies are most in demand in the country's agriculture, study the change in demand for advanced digital technologies, and identify the main constraints for the development of digital agriculture.
{"title":"DIGITAL TRANSFORMATION OF AGRICULTURE: PRIORITIES AND BARRIERS","authors":"Daina Vasilevska, B. Rivza","doi":"10.5593/sgem2022/2.1/s07.04","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s07.04","url":null,"abstract":"In the practice of leading countries, digital transformation implies the industry principle of supporting the introduction of new technologies. At the same time, programs and projects can be built both on the principle of introducing any one promising group of technologies in several industries, where it is especially relevant, and provide for the digital transformation of a particular industry through the forced introduction of many technologically heterogeneous solutions that are in demand in a given sector of the economy or social sphere. Digital transformation contributes to solving systemic problems in industries, reorganizing labor and automating routine tasks. Thus, in the electric power industry, the number of accidents at infrastructure facilities is reduced, in construction - the level of injuries at the construction site. Digital products improve coordination within and between organizations and reduce transaction costs. For example, platform solutions in logistics reduce the role of intermediaries, while expanding the possibilities for communication with end users. The digital transformation of agriculture is largely based on the integrated implementation of a number of digital technologies within the interrelated concepts of precision farming and smart agriculture. The purpose of this study is to determine the level of digitalization of agriculture in Latvia. Determine which digital technologies are most in demand in the country's agriculture, study the change in demand for advanced digital technologies, and identify the main constraints for the development of digital agriculture.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"110 4-5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114048260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s07.12
Marta Blahová
Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using the prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The result is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. One of the chapters of the article describes the possibilities of using these elements. It is proved that in addition to classical optimization algorithms, it is also possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of solved problem and settings. The article describes a suitable selection of model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematicalphysical description is not known. The chapter of the article also deals with the possibility of using the found models on real systems and determining the necessary conditions and requirements for their application.
{"title":"USE OF ARTIFICIAL INTELLIGENCE ELEMENTS IN PREDICTIVE PROCESS MANAGEMENT","authors":"Marta Blahová","doi":"10.5593/sgem2022/2.1/s07.12","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s07.12","url":null,"abstract":"Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using the prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The result is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. One of the chapters of the article describes the possibilities of using these elements. It is proved that in addition to classical optimization algorithms, it is also possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of solved problem and settings. The article describes a suitable selection of model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematicalphysical description is not known. The chapter of the article also deals with the possibility of using the found models on real systems and determining the necessary conditions and requirements for their application.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125597999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s11.45
I. Catianis, G. Iordache, A. Constantinescu, A. Pavel, O. Dobre
This paper presents the findings of the mapping and evaluation of macrophyte vegetation from Tataru and Uzlina lakes, located within Danube Delta, Romania. Throughout time, the Danube Delta unique ecosystem has endured considerable ecological changes due to natural and anthropogenic stressors. Currently, the potential threats are those related to pollution, agriculture, land use, climate change and subsequently, the proliferation and/or the extinction of many macrophyte species. The excessive development of aquatic vegetation has negative consequences, reducing the flow velocity of water in the connecting channels, exacerbates the sedimentation processes in the deltaic lakes which are subject to the clogging phenomenon (i.e., silting). The abundance, distribution and biodiversity of the emergent aquatic species were surveyed in Tataru and Uzlina lakes. The biodiversity of macrophytes is different in the investigated lakes, being dependent on the fluvial input of water and sediment of the Danube River, extent of lakes and water depth. In Tataru Lake, the macrophyte vegetation was represented by a greater variety of species (Nymphaea alba, Nuphar lutea, Trapa natans, Salvinia natans, Lemna minor and Phragmites australis), in comparison to Uzlina Lake (Nymphaea alba, Nuphar lutea, Trapa natans and Phragmites australis). This methodology and results are valuable for the strategic environmental assessment and management of the Danube Delta Biosphere Reserve and can be used in similar settings.
{"title":"ASSESSMENT OF THE ABUNDANCE AND DISTRIBUTION OF THE EMERGENT AQUATIC VEGETATION IN THE DANUBE DELTA, ROMANIA. THE CASE STUDY OF TATARU AND UZLINA LAKES","authors":"I. Catianis, G. Iordache, A. Constantinescu, A. Pavel, O. Dobre","doi":"10.5593/sgem2022/2.1/s11.45","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s11.45","url":null,"abstract":"This paper presents the findings of the mapping and evaluation of macrophyte vegetation from Tataru and Uzlina lakes, located within Danube Delta, Romania. Throughout time, the Danube Delta unique ecosystem has endured considerable ecological changes due to natural and anthropogenic stressors. Currently, the potential threats are those related to pollution, agriculture, land use, climate change and subsequently, the proliferation and/or the extinction of many macrophyte species. The excessive development of aquatic vegetation has negative consequences, reducing the flow velocity of water in the connecting channels, exacerbates the sedimentation processes in the deltaic lakes which are subject to the clogging phenomenon (i.e., silting). The abundance, distribution and biodiversity of the emergent aquatic species were surveyed in Tataru and Uzlina lakes. The biodiversity of macrophytes is different in the investigated lakes, being dependent on the fluvial input of water and sediment of the Danube River, extent of lakes and water depth. In Tataru Lake, the macrophyte vegetation was represented by a greater variety of species (Nymphaea alba, Nuphar lutea, Trapa natans, Salvinia natans, Lemna minor and Phragmites australis), in comparison to Uzlina Lake (Nymphaea alba, Nuphar lutea, Trapa natans and Phragmites australis). This methodology and results are valuable for the strategic environmental assessment and management of the Danube Delta Biosphere Reserve and can be used in similar settings.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-15DOI: 10.5593/sgem2022/2.1/s09.20
S. Gospodinov, E. Peneva, Penio S. Penev
In addition to its main purpose: establishing and maintaining the parameters of the height system for the territory of a given state (country), state levelling networks also serve to establish (register) the contemporary (recent) vertical movements of the Earth's crust. The detection of such movements, besides in a purely research sense, is of great practical importance. The displacement of the benchmarks over time plays an essential role in seismic forecasting in the short and long term. Sometimes, not very often, it happens that the duration of the measurements in a single cycle of State levelling network measurement is commensurate or nearly commensurate with the period between the different cycles. Such a fact raises serious issues to be addressed, both in the process of preliminary accuracy estimation of the measurements and in the formation of the adjustment model. A period of ten years or more is long enough for displacements on the order of a few mm (millimeters) to become apparent and to be reliably detected. One possible approach, in such cases, is to apply a modified version of adjustment using the Least Squares Method. It would be appropriate, as additional unknowns, to introduce the velocities of the individual benchmarks of the network into the adjustment model. Thus, taking into account the time of the start of the measurements, preconditions are created for taking into account the dynamic behaviour of the benchmarks during the measurement period. The applied adjustment model is based on the so-called "dynamic" or "kinematic" adjustment model, which also takes into account some technological features in the overall network measurement process.
{"title":"A SPECIFIC APPROACH TO LEAST SQUARES ADJUSTMENT OF THE STATE LEVELLING NETWORK","authors":"S. Gospodinov, E. Peneva, Penio S. Penev","doi":"10.5593/sgem2022/2.1/s09.20","DOIUrl":"https://doi.org/10.5593/sgem2022/2.1/s09.20","url":null,"abstract":"In addition to its main purpose: establishing and maintaining the parameters of the height system for the territory of a given state (country), state levelling networks also serve to establish (register) the contemporary (recent) vertical movements of the Earth's crust. The detection of such movements, besides in a purely research sense, is of great practical importance. The displacement of the benchmarks over time plays an essential role in seismic forecasting in the short and long term. \u0000Sometimes, not very often, it happens that the duration of the measurements in a single cycle of State levelling network measurement is commensurate or nearly commensurate with the period between the different cycles. Such a fact raises serious issues to be addressed, both in the process of preliminary accuracy estimation of the measurements and in the formation of the adjustment model. A period of ten years or more is long enough for displacements on the order of a few mm (millimeters) to become apparent and to be reliably detected. \u0000One possible approach, in such cases, is to apply a modified version of adjustment using the Least Squares Method. It would be appropriate, as additional unknowns, to introduce the velocities of the individual benchmarks of the network into the adjustment model. Thus, taking into account the time of the start of the measurements, preconditions are created for taking into account the dynamic behaviour of the benchmarks during the measurement period. \u0000The applied adjustment model is based on the so-called \"dynamic\" or \"kinematic\" adjustment model, which also takes into account some technological features in the overall network measurement process.","PeriodicalId":375880,"journal":{"name":"22nd SGEM International Multidisciplinary Scientific GeoConference Proceedings 2022, Informatics, Geoinformatics and Remote Sensing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121764005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：