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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 9918

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Special Issue Editors

Dr. Yao-Min Fang

E-Mail Website
Guest Editor

College of Construction and Development, Feng Chia University, Taichung 407102, Taiwan
Interests: disaster monitoring; smart cities; civil engineering

Dr. Thanh-Van Hoang

E-Mail Website
Guest Editor

Department for Infrastructure and Engineering, College of Construction and Development, Feng Chia University, Taichung 407102, Taiwan
Interests: climate change; natural hazards; GIS and RS applications in environmental management; geoinformatics; UAV applications; vulnerabilities; capacities and risk

Dr. Duc-Dung Tran

E-Mail Website
Guest Editor

National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore 639798, Singapore
Interests: water resources management; climate change; socio-hydrology; applied geographic information systems

Special Issue Information

Dear Colleagues,

A synchronously and modernly developed infrastructure system will promote economic growth, improve the productivity and efficiency of the economy, and contribute to solving social problems. Challenges remain in boosting critical infrastructure resilience in a dynamic risk landscape; toward this aim, this Special Issue aims to publish papers addressing topics including climate-resilience and sustainability, urban resilience and urban sustainability, critical infrastructure, sector risk management agency for water and wastewater systems, and the achievement of sustainability in specific urban sectors (e.g., public health, water, and infrastructure).

The key objective of this Special Issue is to collect updated research on Urban Resilience and Critical Infrastructure. The following relevant topics are also welcome for submission to this Special issue:

Flooding prediction in sustainability;
Big data in GIS and remote sensing;
AI in disaster prevention technology;
Hydro and water resource;
Resource conversation;
Urban resilience;
Urban infrastructure;
VA/AR;
Climate change;
Internet of Things.
Three-dimensional GIS and RS in Earth surface modeling.

Dr. Yao-Min Fang
Dr. Thanh-Van Hoang
Dr. Duc-Dung Tran
Guest Editors

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

urban resilience
urban infrastructure
flooding prediction in sustainability
3D GIS and RS in Earth-surface modeling
AI in disaster prevention technology

Published Papers (7 papers)

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Research

21 pages, 4905 KiB

Open AccessArticle

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

by Van Manh Do, Quang Hoc Tran, Khanh Giang Le, Xuan Can Vuong and Van Truong Vu

Sustainability 2024, 16(6), 2453; https://doi.org/10.3390/su16062453 - 15 Mar 2024

Viewed by 611

Abstract

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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39 pages, 24534 KiB

Open AccessArticle

Using Immersive Virtual Reality to Study Road-Crossing Sustainability in Fleeting Moments of Space and Time

by Paul M. Torrens and Ryan Kim

Sustainability 2024, 16(3), 1327; https://doi.org/10.3390/su16031327 - 04 Feb 2024

Cited by 2 | Viewed by 1539

Abstract

Despite a history of year-by-year reduction in road-crossing harm and fatality in the United States, the trend reversed course in 2009 and road-crossing has grown more hazardous since. Within this tendency, there has been a marked uptick in risk to urban crossers who are neither children nor elderly. The age group in between these extremes represents a bulk of urban crossers, for whom theoretical explanations for crossing behavior that are focused on youth and senior crossing factors often do not apply. New insight is likely required to explain why the rate of crossing harm is growing for the 20–44 age group, but declining among the young and elderly. However, it is difficult to experiment with crossing scenarios in a real-world context, where significant dangers are present and for which the uniqueness of crossers and crossing sites is abundant. In this paper, we introduce an end-to-end system for examining crossing behavior using a unique combination of real human crossing behavior, made safe through the combination of agent-based models, motion capture, virtual geographic environments, and immersive technologies from virtual reality. We demonstrate that this combination of methods can be deployed to examine very high resolution and very high specificities of crossing scenarios and behaviors, with reach to individual crossers and their judgment over tiny windows of space and time. We demonstrate that the system can reproduce known effects from the theoretical literature and from existing case studies, while also generating huge swaths of empirical and diagnostically useful data on crossing actions, interactions, and reactions relative to fleeting events and phenomena of urban geography, traffic dynamics, and ambient pedestrian crowds. To prove the concept, we deploy the system to investigate crossing judgment behavior among the 20–44 age group. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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19 pages, 1944 KiB

Open AccessArticle

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

by Khanh Giang Le, Quang Hoc Tran and Van Manh Do

Sustainability 2024, 16(1), 107; https://doi.org/10.3390/su16010107 - 21 Dec 2023

Viewed by 721

Abstract

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (

C_{f}

≥ 0.9 and

L_{t}

≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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26 pages, 7295 KiB

Open AccessArticle

Transfer-Ensemble Learning: A Novel Approach for Mapping Urban Land Use/Cover of the Indian Metropolitans

by Prosenjit Barman, Sheikh Mustak, Monika Kuffer and Sudhir Kumar Singh

Sustainability 2023, 15(24), 16593; https://doi.org/10.3390/su152416593 - 06 Dec 2023

Viewed by 1733

Abstract

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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19 pages, 2935 KiB

Open AccessArticle

A Quantitative Evaluation Model for the Seismic Resilience of Water Supply Systems Based on Fragility Analysis

by Houli Wu, Endong Guo, Peilei Yan and Jingyi Liu

Sustainability 2023, 15(16), 12137; https://doi.org/10.3390/su151612137 - 08 Aug 2023

Viewed by 1017

Abstract

A quantitative evaluation model is proposed to assess the seismic resilience of water supply systems. The water supply system is divided into three parts: water sources, aboveground infrastructures, and underground pipeline network, and importance factors for the different parts are quantified. Resilience demand is expressed as the desirable functionality loss and the recovery time of the water supply system after an earthquake. First, seismic fragility models are established for the different components of the water supply system. A water quality index is utilized to represent the impact of earthquakes on the water sources, the seismic performances of aboveground infrastructures are represented by fragility curves, and the repair rate in terms of number of repairs per kilometer is adopted for the pipeline network. Then, the post-earthquake functionality of the water supply system is quantified based on seismic fragility analysis. Changes in the water quality index are used to indicate the functionality losses related to water sources, the functionality losses of aboveground infrastructures are represented by the economic losses derived from component fragility curves, and post-earthquake functionality losses in the underground pipeline network are quantified by hydraulic simulations. The functionalities of the three parts are calculated separately, and then the overall system functionalities are obtained as the sum of the weighted functionalities of the three parts. Finally, a repair strategy is developed and the recovery time is calculated considering the system damage scenarios, system functionality analyses, and resource reserves. The proposed resilience assessment model considers all components of the water supply system, and the results are reliable when the basic information is complete and accurate. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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19 pages, 10016 KiB

Open AccessArticle

Assessing Climate-Driven Salinity Intrusion through Water Accounting: A Case Study in Ben Tre Province for More Sustainable Water Management Plans

by Nguyen Trung Nam, Pham Thi Bich Thuc, Do Anh Dao, Nguyen Duc Thien, Nguyen Hai Au and Dung Duc Tran

Sustainability 2023, 15(11), 9110; https://doi.org/10.3390/su15119110 - 05 Jun 2023

Viewed by 1394

Abstract

This scientific paper delves into sustainable water management strategies for Ben Tre Province of the Vietnamese Mekong Delta (VMD) in light of water-infrastructure plans that have been impacted by climate change-induced salinity intrusion. Specifically, we aim to mitigate the effects of salinity intrusion for the province while promoting long-term environmental sustainability. In doing so, a water accounting framework was applied, mostly based on the MIKE11 hydrodynamic modeling and water balance calculations, to determine current and future water stress issues based on two main scenarios of extreme drought year 2016 (baseline) and the future year 2030 under climate change for a medium-low emission scenario (RCP4.5). The study found that salinity intrusion significantly causes severe water stress in the future year 2030 compared to the baseline year 2016, while the existing water management methods are relatively inadequate to control salinity intrusion, leading to over 57% of the area affected by medium to critical water stress levels, although it will go along with planned water infrastructures. Additionally, a system of triple rice cropping converted two rice cropping and upland cropping with 40% water demand cutoff was found to be the most suitable measure for 2030. Particularly, water-saving and water demand reduction should be incorporated into infrastructural planning for sustainable water management. Our study provides valuable insights for policymakers and stakeholders, not only for the province and the VMD, but also other regions facing similar challenges. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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17 pages, 3301 KiB

Open AccessArticle

The Combination of Anaerobic Digestion and Electro-Oxidation for Efficient COD Removal in Beverage Wastewater: Investigation of Electrolytic Cells

by Huy N. Q. Phan, Jyh Hoang Leu and Vi N. D. Nguyen

Sustainability 2023, 15(6), 5551; https://doi.org/10.3390/su15065551 - 22 Mar 2023

Cited by 4 | Viewed by 1742

Abstract

The world’s ever-growing population is driving an increased demand for clean water, which makes treating and reusing wastewater an essential practice. In recent years, biological and physicochemical methods have been preferred for wastewater treatment, with combined systems proving particularly effective. In this study, the combination of anaerobic digestion (AD) and electro-oxidation (EO) was investigated as a process for removing chemical oxygen demand (COD) from actual beverage wastewater. The effect of hydraulic retention time (HRT) on AD, electrolysis time, sodium chloride (NaCl) dosage, initial pH, and electro-properties on EO was investigated. At optimum conditions, namely an HRT of 2 days for AD, NaCl concentration of 3 g L⁻¹, 80 min of EO time, natural pH (7.45), and applied voltage of 20 V for EO, the removal efficiency for COD was an impressive 96.47%, with energy consumption and specific energy consumption calculating 177.33 kWh m⁻³ and 33.79 kWh kgCOD⁻¹, respectively. The amount of by-product gases (CH₄ and H₂) were also meagerly determined in this study. The results confirm that combining the AD and EO methods is an effective COD removal solution that can benefit the industry, while also offering a sustainable solution to combat water scarcity and meet the growing demand for clean water. Full article

(This article belongs to the Special Issue Urban Resilience and Critical Infrastructure)

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