Environmental Data Management Research Articles

Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis

This research focuses on developing an innovative platform to manage carbon footprint data related to office activities and monitor green spaces, integrating geoinformatics and machine learning technologies. The platform addresses the lack of automated systems for tracking carbon emissions, particularly in high paper consumption environments, such as those involving printing and photocopying. Additionally, it monitors green spaces within corporate settings, an aspect often overlooked in existing systems. The study demonstrates the platform’s capability to automate carbon footprint calculations and provide accurate assessments of green areas, achieving a high accuracy rate of 96.22% and a Kappa coefficient of 0.92. The results confirm the platform’s ability to deliver both comprehensive and granular environmental insights, supporting decision making for more sustainable office environments. The key novelty of this study lies in the integration of real-time data capture with geoinformatics and machine learning to efficiently track both carbon footprints and green spaces. This approach offers a practical solution to a gap in environmental data management in office settings, enabling organizations to align their practices with sustainability goals. The platform’s precise, automated system contributes significantly to the development of eco-friendly workplaces, highlighting its academic and practical value in the field of environmental sustainability.

Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA)

AbstractClustering and machine learning‐based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data‐rich and data‐poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field‐measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field‐measured and geospatial data decreases. This suggests that mismatched scales between field‐derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade‐off between uniformly capturing geomorphic variability and ensuring robust generalisation.

Environmental Monitoring Data Management and Analysis Based on 5G IoT

As environmental issues become increasingly severe, environmental monitoring has become a crucial measure for protecting and managing ecosystems. The high speed, low latency, and massive connectivity characteristics of 5G technology, combined with the widespread application of IoT technology, provide new solutions for environmental monitoring data management and analysis. This study explores the methods of environmental monitoring data management and analysis based on 5G IoT technology, designs a systematic environmental monitoring framework, and verifies its effectiveness through empirical research. The results show that the environmental monitoring system based on 5G IoT can efficiently, real-time, and reliably collect and analyze environmental data, providing strong support for environmental protection and management.

Secure environmental, social, and governance (ESG) data management for construction projects using blockchain

Environmental, social, and governance (ESG) considerations are increasingly becoming imperative and obligatory across various industries. The ESG performance within the architecture, engineering, and construction (AEC) industry is under heightened market scrutiny. However, current ESG management in construction is still in its infancy due to two limitations: (1) a deficiency in ESG knowledge, such as indicators pertinent to construction activities, and (2) a lack of data security in ESG management, culminating in inefficient and unreliable environmental management practices. Therefore, this paper employs the Design Science Research Method (DSRM) to introduce a Blockchain-ESG Integrated (BESGI) framework, facilitating traceable ESG data management within construction projects. This framework presents three significant contributions. First, it identifies ten AEC-ESG indicators by analyzing ESG methods. Second, it proposes a mapping approach for AEC-ESG indicators to construction projects for key ESG information access and data source identification. Third, it develops a blockchain-based data management mechanism for traceable ESG data management in the BESGI framework. It validates and evaluates the framework in a construction project in Hong Kong. The results show that the framework is usable and can save labor costs by 20.15 % compared to traditional ESG management. This study offers a secure data management solution for ESG analysis of construction projects.

Assessing external exposome by implementing an Environmental Data Management System using Open Data

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017–2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Łódź for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Łódź the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions.

Unified data integration and record identification framework for diverse data sources in healthcare demographics

Challenges of bridging data from different sources with diverse data formats are faced by organizations in the modern data management environment. Problems with disparate data sources leading to different formats and inconsistencies mean it can be challenging to get the right matching of data records, especially when information errors such as typos are present. The current lack of a standard pattern for data integration and record identification presents a major problem in ensuring the accurate identification of individual records across disparate sources. The variations in data formats and the abundance of errors, such as typographical mistakes in names, dates of birth, and gender, add to the complexity of this problem. Organizations face the challenge of ensuring the correctness and consistency of data across multiple datasets without a formalized methodology.

Effective Data Migration Strategies Using Talend and DataStage

Modern company operations need reliable data migration across systems to preserve continuity, performance, and business process integrity. Data transfer techniques need careful preparation, powerful tools, and system knowledge. Talend and IBM DataStage, two renowned data integration and migration systems, are used in this study to implement successful data transfer techniques. Talend and DataStage provide ETL, data quality management, and real-time integration services for data migration. Talend, an open-source platform, supports several data sources and formats with flexibility, scalability, and many connections. Organizations seeking cost-effective solutions appreciate it for its real-time data processing, user-friendly interface, and comprehensive data transformation capabilities. Talend's powerful data profiling, cleaning, and governance tools assure high-quality, compliant data migration. IBM DataStage, part of IBM InfoSphere, is a sophisticated ETL tool for large-scale data integration. DataStage is ideal for complicated data settings with big data volumes because to its great performance and scalability. DataStage can handle the most difficult data migration operations due to its parallel processing and interaction with IBM's data management environment. Enterprises who need a dependable data migration solution can benefit from the platform's metadata management, data lineage, and real-time data integration, which interface with many databases and applications.

Concept and implementation of an adaptive digital hearing training system for cochlear implant aftercare

Within the context of an interdisciplinary research project, we created acutting-edge prototype of an adaptive digital auditory training system designed for cochlear implant (CI) users. By leveraging the evidence-centered design (ECD) framework, we integrated adynamic difficulty adjustment feature that tailors the experience to the unique performance capabilities of each individual user. The ECD provides aconceptual design framework suitable for complex assessments of competence and dynamic performance. In the first phase, the domain of hearing was first defined in the context of CI users. In the development phase the three core models of the ECD, the competence model, the evidence model, and the task model, were developed and implemented. In addition, an asset pool of sound and language files was created, which included comprehensive linguistic feature descriptions for calculating item difficulties. Based on the requirements described, an adaptive exercise generator, an AI service, and other components were implemented. This included the development of agame environment and adashboard for patient data management. The exercises' difficulty levels were determined based on various parameters (e.g., sound, word frequency and number of words, grammatical properties) in combination with defined task types and levels. An adaptive digital auditory training system can help to supervise and train CI patients in acontinuous, interactive process based on their individual needs. We see the ECD as an effective way to build auser-based adaptive system.

Camguard: Preventing unauthorised camera access for women’s saftey

Online harassment is a serious problem that can have significant negative impacts on the mental health and well-being of its victims. To address this issue, an Android app has been developed that is accessible to a wide range of users worldwide. The app is built on a script backend process, providing a stable and efficient environment for data management and request processing. The app's API is integrated into the script backend process, ensuring seamless and efficient operation.During the installation process, the app requires users to go through a verification process that includes providing their Google Account, mobile number, and an OTP. This ensures that only legitimate users have access to the app's features and functions. The app also requires users to create a strong master password, which is directly connected to the Google bot, enhancing its security. Biometrics are recommended to protect the master password, ensuring that only authorized users can access their data.The app's security measures are crucial in ensuring the privacy and protection of user data. The use of biometrics and direct connection to the Google bot enhances security, and the app's API provides access to relevant information and enhances the user experience. Users can change their password securely, ensuring that only authorized users can access their data.Overall, the app is a reliable and efficient solution for managing and protecting user data. Its compatibility with a wide range of devices and seamless integration of API make it accessible to a large percentage of Android users worldwide. The app's verification process, use of biometrics, and direct connection to the Google bot enhance its security, ensuring that only authorized users have access to the app's functionalities. The development of this app is a critical step in addressing the issue of online harassment and protecting the privacy and well-being of its users.

The role of BIM and GIS in HS2 historic environment data management, an overview of HS2 Phase 1, UK

The HS2 historic environment programme undertaken for Phase One of HS2 between London and the West Midlands has resulted in a substantial digital archive, including Geographic Information Systems (GIS) data. According to the BIM (Building Information Modelling) approach, HS2 historic environment assets are considered as part of the construction assets, alongside the other disciplines assets such as highways, bridges, tunnels, and fencing. The GIS and spatial data play the role of a glue to demonstrate the interrelationship and hierarchy between archaeological assets, recording their location and geometry. Designating a unique asset ID (UAID) to each archaeological asset, and joining them to their attributes table and relating documents, creates a relationship between historic environment assets GIS data and their respective non-GIS data. According to HS2 digital engineering with BIM approach, HS2 historic environment core and primary assets have been identified, which the hierarchical order of them is as follows: Location Specific Written Scheme of Investigation (LS-WSI) and Project Plans (PPs) as core assets, and Written Scheme of Investigation Interventions (WSI-Interventions), Archaeological Features, and Archaeological Objects as historic environment primary assets. Such an efficient, transparent, and readable asset data structure provides a lasting and valuable legacy for the lifecycle of the project digital data. Four different systems combine to form the digital legacy of the project, which complement the physical archive. These four systems are: HS2 Asset Information Management System (AIMS) and GIS systems, as well as Online Access to the Index of Archaeological Investigations (OASIS), and supporting digital data curated with the Archaeological Data Service (ADS) systems. As significant parts of the programme legacy, HS2's historic environment physical and digital archive establish an unprecedented opportunity for knowledge creation.

Research on GIS-based information value model for landslide geological hazards prediction in soil-rock contact zone in southern Shaanxi

Landslide geological hazards occur frequently and are widely distributed in the soil-rock contact zone in southern Shaanxi Province, which has a great impact on people's lives. To realize the hierarchical management of geological environmental data, the powerful spatial graphic and attribute data management function of GIS is fully utilized. On this basis, the GIS-based landslide geological hazard prediction method for the soil-rock contact zone in southern Shaanxi was developed using the information value model. By dividing the prediction units in the study area, basic components such as the slope distribution map of the soil-rock contact zone, rock group type, and fault distribution map are generated. Then, the prediction model of landslide geological hazard information value is established, combining the information quantity model and GIS technology, and grading the predicted landslide geological hazard information quantity, thus obtaining the prediction results, and completing the prediction of landslide geological hazard in the soil-rock contact zone in southern Shaanxi. The results show that when the slope of the study area is between 6° and 25°, the proportion of landslide disaster prediction is higher, Rainfall occurring on the 4th day in the first 10 days of the landslide disaster has the greatest impact on the landslide disaster. According to the actual landslide disaster prediction results, it is known that the method can accurately predict the type and severity of landslide occurrence each month, and the accuracy of the prediction results is high, making it a high-quality landslide hazard prediction method.

Spacecraft Test Data Integration Management Technology based on Big Data Platform

In this paper, a general test platform for spacecraft data management is designed and constructed. This paper introduces a portable software development environment based on LUA. The technology of space environment data management, comprehensive analysis, parameter correction and visual display of spacecraft is realized. The relationship between continuity, mixed dispersion, variation and indication of remote sensing data is studied. This project uses the integrated Long Short Term Memory network (LSTM) technology to detect anomalies in satellite remote sensing observation data. Give full play to the advantages of laser scanning tunneling microscope in the nonlinear field. The combination of this method and the matrix method can improve the adaptive ability of spacecraft in an operation state to better identify abnormal information in remote sensing data. Experiments show that the algorithm can significantly improve the anomaly detection rate of the system. The system can monitor the front test device and record the data. The method can be connected with the space vehicle’s central control and automatic test system. The comprehensive management of the integrated test system of space vehicles is realized.

A concept for application of integrated digital technologies to enhance future smart agricultural systems

Future agricultural systems should increase productivity and sustainability of food production and supply. For this, integrated and efficient capture, management, sharing, and use of agricultural and environmental data from multiple sources is essential. However, there are challenges to understand and efficiently use different types of agricultural and environmental data from multiple sources, which differ in format and time interval. In this regard, the role of emerging technologies is considered to be significant for integrated data gathering, analyses and efficient use. In this study, a concept was developed to facilitate the full integration of digital technologies to enhance future smart and sustainable agricultural systems. The concept has been developed based on the results of a literature review and diverse experiences and expertise which enabled the identification of stat-of-the-art smart technologies, challenges and knowledge gaps. The features of the proposed solution include: data collection methodologies using smart digital tools; platforms for data handling and sharing; application of Artificial Intelligent for data integration and analysis; edge and cloud computing; application of Blockchain, decision support system; and a governance and data security system. The study identified the potential positive implications i.e. the implementation of the concept could increase data value, farm productivity, effectiveness in monitoring of farm operations and decision making, and provide innovative farm business models. The concept could contribute to an overall increase in the competitiveness, sustainability, and resilience of the agricultural sector as well as digital transformation in agriculture and rural areas. This study also provided future research direction in relation to the proposed concept. The results will benefit researchers, practitioners, developers of smart tools, and policy makers supporting the transition to smarter and more sustainable agriculture systems.

An integrated system for the management of environmental data to support veterinary epidemiology.

Environmental and climatic fluctuations can greatly influence the dynamics of infectious diseases of veterinary concern, or interfere with the implementation of relevant control measures. Including environmental and climatic aspects in epidemiological studies could provide policy makers with new insights to assign resources for measures to prevent or limit the spread of animal diseases, particularly those with zoonotic potential. The ever-increasing number of technologies and tools permits acquiring environmental data from various sources, including ground-based sensors and Satellite Earth Observation (SEO). However, the high heterogeneity of these datasets often requires at least some basic GIS (Geographic Information Systems) and/or coding skills to use them in further analysis. Therefore, the high availability of data does not always correspond to widespread use for research purposes. The development of an integrated data pre-processing system makes it possible to obtain information that could be easily and directly used in subsequent epidemiological analyses, supporting both research activities and the management of disease outbreaks. Indeed, such an approach allows for the reduction of the time spent on searching, downloading, processing and validating environmental data, thereby optimizing available resources and reducing any possible errors directly related to data collection. Although multitudes of free services that allow obtaining SEO data exist nowadays (either raw or pre-processed through a specific coding language), the availability and quality of information can be sub-optimal when dealing with very small scale and local data. In fact, some information sets (e.g., air temperature, rainfall), usually derived from ground-based sensors (e.g., agro-meteo station), are managed, processed and redistributed by agencies operating on a local scale which are often not directly accessible by the most common free SEO services (e.g., Google Earth Engine). The EVE (Environmental data for Veterinary Epidemiology) system has been developed to acquire, pre-process and archive a set of environmental information at various scales, in order to facilitate and speed up access by epidemiologists, researchers and decision-makers, also accounting for the integration of SEO information with locally sensed data.

DATA MANAGEMENT ENVIRONMENT PROPERTIES INVESTIGATION AND TIME ESTIMATION OF LARGE DATA SET TRANSFER

The article considers the task of estimating the time of transmission of large data sets through a distributed data management environment based on a self-created neural network model and investigating the properties of this environment using statistical analysis methods. For the initial analysis, metadata for successful file transfers in the system was obtained, variables that affect file transfer time were transformed and highlighted. Different samples were used in the analysis to check whether the results were similar across the available data. The methods of correlation and regression analysis are applied for the study of the environment. It was found that there is no clear correlation between the transmission time and one of the input parameters. The file transfer time depends on a number of external factors that cannot be obtained using metadata, but it is possible to partially investigate the environment using the obtained metadata. A model based on two input levels for numerical and categorical variables was used and then combined into one branch. RMSE metric value and a scatter plot are used to display the prediction results to compare the target and predicted values. The performed calculations show satisfactory prediction results;

Trending environmental monitoring data, analysis and data management for non-sterile pharmaceutical manufacturing

Trending environmental monitoring data, analysis and data management for non-sterile pharmaceutical manufacturing

Development of a Knowledge System for Data Management of the Pre-Production Stages

Preparation of projects introducing new production takes into account all aspects related to development and technical preparation of production, production of prototypes, familiarization with production, processing marketing sales support, as well as preparation of service support. PLM systems represent the tools for automating engineering activities in the product life cycle. These solutions are based on the idea of a close cooperation between individual areas involved in the final product, from its design to the end of its technical life. Collecting a large amount of data and information about the product under observation is typical for this kind of activity. PLM integrates the areas of computer aid, database and information systems, and computer networks. This article points to the creation of a knowledge-based application environment for data management of a small business with a description of its functions and possibilities of use.

Modeling of spatiotemporal data about the environment in GIS

A large number of sensors are used to monitor the environment, and a large volume of spatio-temporal data on epizootic risks and the environment is processed in real time. To date, GIS data models are represented by static models and more modern time models. However, many of the epizootological and environmental data management systems do not meet the requirements of real-time data management. The purpose of the work is to propose, based on the analysis of foreign literature sources, a modern method for managing epizootological and environmental data based on a new GIS model in real time in comparison with the Sensor web service model.Two experiments were conducted in the urban environment and on the territories of livestock farms with different epizootic situations for potential risk management in zoonoses. Real-time monitoring of air quality and real-time monitoring of soil moisture was carried out in Wuhan (China). The circulation of pathogens of zoonoses and sapronoses in the environment, including in the soil, and their preservation in the form of spores and hard-to-cultivate forms, determines the ecological component of emergent epizootics and epidemics with the coverage of new areas. Experimental results have shown that the use of the proposed GIS data model on the Sensor web service platform for managing epizootological/epidemiological and environmental data in real time is reliable and effective.

STRATEGY ANALYSIS OF HANDLING CIRCULATION AND ABUSE USING SWOT METHOD AT CLASS IIB RUTAN PRABUMULIH

This research is motivated by the increasing number of prisoners and convicts of narcotics crimes. So, it can grab the attention of the public and the government. This is based on several cases of narcotics trafficking that occurred in the correctional task force unit (UPT). The purpose of this study was to find out how the environmental conditions of the correctional task force unit (UPT) internally and externally in the strategy of dealing with drug trafficking in the Class IIB Rutan Prabumulih. This research is qualitative in nature which describes or describes the object under study and the observed phenomena on the phenomena that occur in the field. SWOT analysis is used to formulate service strategy. The results of the data management of the internal environmental strategy factors and the results of the external environmental strategy factor data management show that the condition of the prison organization is in a weak condition facing the challenges of narcotics trafficking in prisons. Therefore, the recommended strategy is a defensive or defensive strategy.
 Keywords:
 Drug Trafficking, Prisons, SWOT.

IFC DATA MAPPING BASED ON A PARAMETRIC BIM CODING FOR AN EFFICIENT WORKFLOW IN THE QUANTIFICATION AND MANAGEMENT OF CONSTRUCTION COSTS

Abstract. In recent years there has been a great effort by the agents of the AECO sector aimed at an efficient use of data interoperability between the participating disciplines and the applicability in the specific software. In this context, the BIM (Building Information Modeling) methodology, moves in a collaborative data management environment to monitor and control the design, planning, cost control, construction processes. and building maintenance. But to enjoy the benefits of BIM in its fifth dimension (5D), cost and time savings, it is essential to properly plan each phase of the project and manage the information effectively within the same team.Therefore, this research aims to validate an effective method for the creation of the information of a constructive entity using codes, disaggregated by budget line items, necessary for the mapping of data in the structure of the IFC file (Industry Foundation Classes) and the confrontation with the filtering rules in quantification programs, in 1 to 1 relationship. Under normal conditions, the item to be measured would have a quality or parameter that distinguishes it from another item. All elements are mapped following an IFC structure using the eCOB standard. This information will be collected in the quantification program to compare it with the information established in the rule corresponding to each item.The analyzed and tested method, based on a proper structuring and classification of data of the elements that make up the BIM model, has been productive and efficient in avoiding random decision-making by the operator who analyzes the quantifications and that may lead to errors or inaccuracies when applying the metric. The result of the study shows that the combinations of parameters, influences by the possible variables of the constructive element, can be considerably reduced. The percentage of rules would decrease by a ratio of 17/120 in the IFC information mapping process to be validated in quantification and valuation software, improving data exchange and interoperability in an international environment.