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Sustainability
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Sustainable Risk Assessment Based on Big Data Analysis Methods
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Sustainable Risk Assessment Based on Big Data Analysis Methods

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10150

Special Issue Editor

Prof. Dr. Jin Wang

E-Mail Website
Guest Editor
The School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: Mobile Autonomous Network (MANET); Internet of Vehicles (VANET); Wireless Sensor Network (WSN); Internet of Things (IoT) and its applications

Special Issue Information

Dear Colleagues,

The progress of human scientific and technological civilization promotes the development of computer science, which in turn promotes the progress of human society and science and technology. Big data provide records for this progress and can provide risk assessment and prediction for all walks of life, so as to help human society develop in a favorable direction. It would be very promising to use resources in the ecological environment to help sustainable development.

The use of big data technology for sustainable risk assessment of the ecological environment, a process that covers data collection, storage, mining, protection, and analysis, aims to help to solve environmental, resource, and energy conservation problems and provide new solutions for sustainable development.

Transforming big data into a usable state takes time. Once they are ready, advanced analytics processes can turn big data into big insights. This field continues to evolve as data engineers look for ways to integrate the vast amounts of complex information created by sensors, networks, transactions, smart devices, web usage, and more. Even now, big data analytics methods are being used with emerging technologies, such as machine learning, to discover and scale more complex insights.

In this context, we are seeking contributions that advance the state of sustainable risk assessment based on big data analysis methods.

Topics of interest for this Special Issue include (but are not limited to):

  1. Sustainable risk assessment models based on big data;
  2. Big data analysis technology for environmental protection;
  3. Big data analytics for resource conservation;
  4. Big data analysis technology for energy conservation;
  5. Big data analytics for intelligent transportation systems;
  6. Sustainable risk assessment models for security based on big data analysis;
  7. Big data analysis technology for environmental pollution and pollution control;
  8. Big data analysis for ecology and biodiversity;
  9. Big data analysis for sustainable risk assessment for policy, planning, regulation, and economics;
  10. Secure data storage and collection in environmental protection;
  11. Data mining, predictive analytics, and deep learning methods for sustainable risk assessment.

Prof. Dr. Jin Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analysis
  • sustainable risk assessment
  • data mining
  • predictive analytics
  • deep learning

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Published Papers (4 papers)

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Research

19 pages, 3203 KiB  
Article
Sustainable Low-Carbon Layout of Land around Rail Transit Stations Based on Multi-Modal Spatial Data
by Weiwei Liu, Jin Zhang, Liang Jin, Jieshuang Dong, Osama Alfarraj, Amr Tolba, Qian Wang and Yihao He
Sustainability 2023, 15(12), 9589; https://doi.org/10.3390/su15129589 - 14 Jun 2023
Cited by 1 | Viewed by 1855
Abstract
With the ever-increasing demand for transport in modern cities, emissions from urban transport are rising. The proportion of carbon emissions in exhaust gas accounts for a large share of society’s total carbon emissions and is increasing. Therefore, urban transport has a sustainable responsibility [...] Read more.
With the ever-increasing demand for transport in modern cities, emissions from urban transport are rising. The proportion of carbon emissions in exhaust gas accounts for a large share of society’s total carbon emissions and is increasing. Therefore, urban transport has a sustainable responsibility to reduce carbon emissions. Investigating the factors that influence carbon emissions from transport has become an important practical issue that needs to be addressed. This paper adopts a “bottom-up” theoretical calculation method of transport carbon emissions and establishes the basic distribution model of inter-modal land use around rail transit stations. It clarifies the connection mode of rail transit stations and establishes the distribution model of carbon emission of stations under different building distribution modes, suggesting the planning of building distribution patterns around rail transit stations. This paper proposes a new method to analyze the influencing factors of carbon emissions at rail transit stations based on multi-modal spatial data in order to make full use of the dense characteristics of rail transit stations and reduce carbon emissions. Full article
(This article belongs to the Special Issue Sustainable Risk Assessment Based on Big Data Analysis Methods)
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19 pages, 14368 KiB  
Article
A Novel Joint Time-Frequency Spectrum Resources Sustainable Risk Prediction Algorithm Based on TFBRL Network for the Electromagnetic Environment
by Shuang Li, Yaxiu Sun, Yu Han, Osama Alfarraj, Amr Tolba and Pradip Kumar Sharma
Sustainability 2023, 15(6), 4777; https://doi.org/10.3390/su15064777 - 8 Mar 2023
Cited by 7 | Viewed by 2024
Abstract
To protect the electromagnetic environment and understand its current state in a timely manner, monitoring the electromagnetic environment has great practical significance, while massive amounts of data are generated. It is crucial to utilize data mining technology to extract valuable information from these [...] Read more.
To protect the electromagnetic environment and understand its current state in a timely manner, monitoring the electromagnetic environment has great practical significance, while massive amounts of data are generated. It is crucial to utilize data mining technology to extract valuable information from these massive amounts of data for effective spectrum management. Traditional spectrum prediction methods do not integrate the prior information of spectrum resource occupancy, so that the prediction of the channel state of a single frequency point is of limited significance. To address these issues, the paper describes a dynamic threshold algorithm which mines bottom noise and spectrum resource occupancy from massive electromagnetic environment data. Moreover, the paper describes a joint time-frequency spectrum resource prediction algorithm based on the time-frequency block residual LSTM (TFBRL) network, which utilizes hourly time closeness, daily period, and annual trend as prior knowledge of spectrum resources. The TFBRL network comprises three main parts: (1) a residual convolution network with a squeeze-and-excitation (SE) attention mechanism, (2) a long short term memory (LSTM) model with memory ability to capture sequence latent information, and (3) a feature fusion module based on a matrix to combine time closeness, daily period, and annual trend feature components. Experimental results demonstrate that the TFBRL network outperforms the baseline networks, improving by 31.37%, 16.00% and 13.06% compared with the best baseline for MSE, RMSE and MAE, respectively. Thus, the TFBRL network has good risk prediction performance and lays the foundation for subsequent frequency scheduling. Full article
(This article belongs to the Special Issue Sustainable Risk Assessment Based on Big Data Analysis Methods)
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14 pages, 12659 KiB  
Article
Rational Layout of Taxi Stop Based on the Analysis of Spatial Trajectory Data
by Weiwei Liu, Chennan Zhang, Jin Zhang, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Qian Wang and Yang Tang
Sustainability 2023, 15(4), 3227; https://doi.org/10.3390/su15043227 - 10 Feb 2023
Cited by 3 | Viewed by 2190
Abstract
The implementation of the relevant management system makes the road-parking behavior standardized, while increasing the difficulty of temporary parking of operational vehicles such as taxis. Therefore, in order to improve the relevant management measures and promote the sustainable development of the taxi industry, [...] Read more.
The implementation of the relevant management system makes the road-parking behavior standardized, while increasing the difficulty of temporary parking of operational vehicles such as taxis. Therefore, in order to improve the relevant management measures and promote the sustainable development of the taxi industry, it is necessary to survey the demand for taxi parking and study the layout of taxi stops. To process the GPS data of the taxis, and to extract the loading and unloading positions of the passengers from the spatial trajectory data, big data analysis technology is used. Compared with the data obtained using traditional survey means, the spatial trajectory data reflects the situation of the whole system, which can make the analysis more accurate. K-means cluster analysis was used to determine community demand. Finally, the immune optimization model was used to determine the optimal taxi stand location. The problem of taxi stand location at the level of urban network from two dimensions of quantity and spatial distribution is solved in this paper. The location of 10 taxi stands can not only meet the parking needs of regional taxis, but also reasonably allocate urban resources and promote sustainable development. This study also has a certain reference value for relevant management departments. Full article
(This article belongs to the Special Issue Sustainable Risk Assessment Based on Big Data Analysis Methods)
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17 pages, 2671 KiB  
Article
Big Data Analysis and Prediction of Electromagnetic Spectrum Resources: A Graph Approach
by Han Zhang, Siqi Peng, Jingyu Zhang and Yun Lin
Sustainability 2023, 15(1), 508; https://doi.org/10.3390/su15010508 - 28 Dec 2022
Cited by 9 | Viewed by 3066
Abstract
In the field of wireless communication, the increasing number of devices makes limited spectrum resources more scarce and accelerates the complexity of the electromagnetic environment, posing a serious threat to the sustainability of the industry’s development. Therefore, new effective technical methods are needed [...] Read more.
In the field of wireless communication, the increasing number of devices makes limited spectrum resources more scarce and accelerates the complexity of the electromagnetic environment, posing a serious threat to the sustainability of the industry’s development. Therefore, new effective technical methods are needed to mine and analyze the activity rules of spectrum resources to reduce the risk of frequency conflict. This paper introduces the idea of graphs and proposes a spectrum resource analysis and prediction architecture based on big data. In this architecture, a spatial correlation model of spectrum activities is constructed through feature extraction. In addition, based on this correlation model, a depth learning network based on graph convolution is designed, which uses the prior information of spatial activity to achieve the efficient prediction of spectrum resources. Numerical experiments were carried out on two datasets with different spatial scales. Compared with the best baseline model, the prediction error is reduced by 8.3% on the small-scale dataset and 11.7% on the large-scale dataset. This shows that the proposed method is applicable to different spatial scales and has more obvious advantages in complex scenes with large spatial scales. It can effectively use the results of spatial domain analysis to improve the prediction accuracy of spectrum resources. Full article
(This article belongs to the Special Issue Sustainable Risk Assessment Based on Big Data Analysis Methods)
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