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Keywords = spatiotemporal model

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18 pages, 4594 KiB  
Article
Evaluation of Urban Resilience and Its Influencing Factors: A Case Study of the Yichang–Jingzhou–Jingmen–Enshi Urban Agglomeration in China
by Zhilong Zhao, Zengzeng Hu, Xu Han, Lu Chen and Zhiyong Li
Sustainability 2024, 16(16), 7090; https://doi.org/10.3390/su16167090 (registering DOI) - 18 Aug 2024
Viewed by 414
Abstract
With the increasing frequency of various uncertainties and disturbances faced by urban systems, urban resilience is one of the vital components of the sustainability of modern cities. An indicator system is constructed to measure the resilience levels of the Yichang–Jingzhou–Jingmen–Enshi (YJJE) urban agglomeration [...] Read more.
With the increasing frequency of various uncertainties and disturbances faced by urban systems, urban resilience is one of the vital components of the sustainability of modern cities. An indicator system is constructed to measure the resilience levels of the Yichang–Jingzhou–Jingmen–Enshi (YJJE) urban agglomeration during 2010–2023 based on four domains—economy, ecology, society, and infrastructure. This paper analyzes the spatiotemporal differentiation of resilience in YJJE in conjunction with the entropy weight method, Getis–Ord Gi* model, and robustness testing. Then, the factor contribution model is used to discern key driving elements of urban resilience. Finally, the CA-Markov model is implemented to predict urban resilience in 2030. The results reveal that the values of resilience in YJJE increase at a rate of 3.25%/a and continue to rise, with the differences among cities narrowing over the examined period. Furthermore, the urban resilience exhibits a significant spatially heterogeneity distribution, with Xiling, Wujiagang, Xiaoting, Yidu, Zhijiang, Dianjun, Dangyang, Yuan’an, Yiling, and Duodao being the high-value agglomerations of urban resilience, and Hefeng, Jianli, Shishou, and Wufeng being the low-value agglomerations of urban resilience. The marked heterogeneity of resilience in the YJJE urban agglomeration reflects the disparity in economic progress across the study area. The total amount of urban social retail, financial expenditure per capita, GDP per capita, park green space area, urban disposable income per capita, and number of buses per 10,000 people surface as the key influencing factors in relation to urban resilience. Finally, the levels of resilience among cities within YJJE will reach the medium level or higher than medium level in 2030. Xiling, Wujiagang, Xiaoting, Zhijiang, Dianjun, Dangyang, and Yuan’an will remain significant hot spots of urban resilience, while Jianli will remain a significant cold spot. In a nutshell, this paper can provide scientific references and policy recommendations for policymakers, urban planners, and researchers on the aspects of urban resilience and sustainable city. Full article
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22 pages, 12307 KiB  
Article
Assessing the Effect of Factor Misallocation on Grain Green Production Capacity: A Case Study of Prefecture-Level Cities in Heilongjiang Province
by Xiaoguang Li, Sishu Zhou and Hong Chen
Agriculture 2024, 14(8), 1395; https://doi.org/10.3390/agriculture14081395 - 18 Aug 2024
Viewed by 283
Abstract
Improving the efficiency of factor allocation in food production is the foundation for accelerating the formation of new quality productivity and achieving an agricultural green transformation. However, there has been no scholarly focus on their mechanisms and the interactions involved. This exploration is [...] Read more.
Improving the efficiency of factor allocation in food production is the foundation for accelerating the formation of new quality productivity and achieving an agricultural green transformation. However, there has been no scholarly focus on their mechanisms and the interactions involved. This exploration is an important reference for enhancing the green production capacity of major grain-producing areas. In this study, 13 prefecture-level cities in Heilongjiang Province, China’s largest grain production base, were selected as the research samples. A model for identifying factor misallocation and grain green total factor productivity (AGGTFP) was constructed to identify the spatiotemporal differences in factor misallocation and green total factor productivity. A fixed effects model was used to explore the impact of single-factor misallocation and the interaction of dual-factor misallocation with AGGTFP. The results show that from 2004 to 2022, the AGGTFP in 13 prefecture-level cities in Heilongjiang Province has shown a slow growth trend. The inhibitory effects of single-factor misallocation of land, labor, and capital on green total factor productivity are sequentially enhanced. The interaction effects of capital misallocation and labor misallocation and labor misallocation and land misallocation strengthen the inhibitory effects of misallocation on the AGGTFP. Therefore, it is necessary to further promote the optimization of production factors and improve the green production capacity for grain. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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16 pages, 4253 KiB  
Article
Multi-Stage Burst Localization Based on Spatio-Temporal Information Analysis for District Metered Areas in Water Distribution Networks
by Xiangqiu Zhang, Yongjun Fang, Xinhong Zhou, Yu Shao and Tingchao Yu
Water 2024, 16(16), 2322; https://doi.org/10.3390/w16162322 - 18 Aug 2024
Viewed by 252
Abstract
Burst events in Water Distribution Networks (WDNs) pose a significant threat to the safety of water supply, leading people to focus on efficient methods for burst localization and prompt repair. This paper proposes a multi-stage burst localization method, which includes preliminary region determination [...] Read more.
Burst events in Water Distribution Networks (WDNs) pose a significant threat to the safety of water supply, leading people to focus on efficient methods for burst localization and prompt repair. This paper proposes a multi-stage burst localization method, which includes preliminary region determination and precise localization analysis. Based on the hydraulic model and spatio-temporal information, the effective sensor sequences and monitoring areas of the nodes are determined. In the first stage, the preliminary burst region is determined based on the monitoring region of sensors and the alarm sensors. In the second stage, localization metrics are used to analyze the dissimilarity degree between burst data from the hydraulic model and the monitoring data from the effective sensors at each node. This analysis helps identify candidate burst nodes and determine their localization priorities. The localization model is tested on the C-Town network to obtain comparative results. The method effectively reduces the burst region, minimizes the search region, and significantly improves the efficiency of burst localization. For precise localization, it accurately localizes the burst event by prioritizing the possibilities of the burst location. Full article
(This article belongs to the Section Water-Energy Nexus)
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20 pages, 7753 KiB  
Article
SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction
by Zhuoqing Jiang, Bing Guo, Huihui Zhao, Yangming Jiang and Yi Sun
J. Mar. Sci. Eng. 2024, 12(8), 1424; https://doi.org/10.3390/jmse12081424 - 17 Aug 2024
Viewed by 333
Abstract
Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction [...] Read more.
Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance. Full article
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23 pages, 11934 KiB  
Article
Spatio-Temporal Changes and Driving Mechanisms of Vegetation Net Primary Productivity in Xinjiang, China from 2001 to 2022
by Qiuxuan Xu, Jinmei Li, Sumeng Zhang, Quanzhi Yuan and Ping Ren
Land 2024, 13(8), 1305; https://doi.org/10.3390/land13081305 - 17 Aug 2024
Viewed by 238
Abstract
Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in [...] Read more.
Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives. Full article
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17 pages, 5526 KiB  
Article
Generating Daily High-Resolution Regional XCO2 by Deep Neural Network and Multi-Source Data
by Wenjie Tian, Lili Zhang, Tao Yu, Dong Yao, Wenhao Zhang and Chunmei Wang
Atmosphere 2024, 15(8), 985; https://doi.org/10.3390/atmos15080985 - 16 Aug 2024
Viewed by 187
Abstract
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from [...] Read more.
CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people’s understanding of CO2 spatiotemporal variations, supporting carbon-related research. Full article
(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (2nd Edition))
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18 pages, 2283 KiB  
Article
Exploring the Impact of Cultivated Land Utilization Green Transformation on Agricultural Economic Growth: Evidence from Jiangsu Province in China
by Xiaodong Yu, Qi Wang, Minji Tian and An Ji
Sustainability 2024, 16(16), 7032; https://doi.org/10.3390/su16167032 - 16 Aug 2024
Viewed by 343
Abstract
Against the backdrop of the green transformation of the national economy, this paper takes Jiangsu Province as a case study to explore spatiotemporal characteristics of cultivated land utilization green transformation (CLUGT) and its impact on agricultural economic growth (AEG). In this study, a [...] Read more.
Against the backdrop of the green transformation of the national economy, this paper takes Jiangsu Province as a case study to explore spatiotemporal characteristics of cultivated land utilization green transformation (CLUGT) and its impact on agricultural economic growth (AEG). In this study, a composite index method and a panel regression model are employed, and the findings of this study indicate that: (1) From 2001 to 2021, the CLUGT index exhibited a modest upward trend, registering an average annual growth rate of 7.12%. (2) The CLUGT displayed significant spatial heterogeneity in the study area. High and medium-high-level areas demonstrated significant clustering, primarily concentrated in the central and northern regions of Jiangsu, while low and medium-low-level areas were primarily located in the southern part of the province. (3) The CLUGT exerted a positive impact on AEG. Specifically, for each one-unit increase in the CLUGT index, the AEG index rose by 0.575. Further analysis indicated that for every one unit of increase in the functional and mode transformation dimensions of CLUGT, the AEG index increased by 0.391 and 0.368, respectively, whereas a one-unit increase in the spatial transformation dimension of CLUGT was associated with a 0.169 decrease in the AEG index. Based on these findings, the study advocates for policies that champion the functional and pattern transformation of CLUGT and prioritize the spatial governance of cultivated land to enhance the contribution of CLUGT to AEG. Full article
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27 pages, 2603 KiB  
Article
An End-to-End Deep Learning Framework for Fault Detection in Marine Machinery
by Spyros Rigas, Paraskevi Tzouveli and Stefanos Kollias
Sensors 2024, 24(16), 5310; https://doi.org/10.3390/s24165310 - 16 Aug 2024
Viewed by 312
Abstract
The Industrial Internet of Things has enabled the integration and analysis of vast volumes of data across various industries, with the maritime sector being no exception. Advances in cloud computing and deep learning (DL) are continuously reshaping the industry, particularly in optimizing maritime [...] Read more.
The Industrial Internet of Things has enabled the integration and analysis of vast volumes of data across various industries, with the maritime sector being no exception. Advances in cloud computing and deep learning (DL) are continuously reshaping the industry, particularly in optimizing maritime operations such as Predictive Maintenance (PdM). In this study, we propose a novel DL-based framework focusing on the fault detection task of PdM in marine operations, leveraging time-series data from sensors installed on shipboard machinery. The framework is designed as a scalable and cost-efficient software solution, encompassing all stages from data collection and pre-processing at the edge to the deployment and lifecycle management of DL models. The proposed DL architecture utilizes Graph Attention Networks (GATs) to extract spatio-temporal information from the time-series data and provides explainable predictions through a feature-wise scoring mechanism. Additionally, a custom evaluation metric with real-world applicability is employed, prioritizing both prediction accuracy and the timeliness of fault identification. To demonstrate the effectiveness of our framework, we conduct experiments on three types of open-source datasets relevant to PdM: electrical data, bearing datasets, and data from water circulation experiments. Full article
(This article belongs to the Section Sensor Networks)
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17 pages, 16021 KiB  
Article
Spatio-Temporal Evolution and Multi-Scenario Prediction of Ecosystem Carbon Storage in Chang-Zhu-Tan Urban Agglomeration Based on the FLUS-InVEST Model
by Weiyi Sun and Xianzhao Liu
Sustainability 2024, 16(16), 7025; https://doi.org/10.3390/su16167025 - 16 Aug 2024
Viewed by 364
Abstract
Land use/land cover change has a significant indicative effect on the carbon storage of terrestrial ecosystems. We selected Chang-Zhu-Tan urban agglomeration as the research object, coupled FLUS and InVEST models to explore the changes in land use and carbon storage in the region [...] Read more.
Land use/land cover change has a significant indicative effect on the carbon storage of terrestrial ecosystems. We selected Chang-Zhu-Tan urban agglomeration as the research object, coupled FLUS and InVEST models to explore the changes in land use and carbon storage in the region from 2010 to 2020, and predicted their spatiotemporal evolution characteristics under three scenarios in 2035: natural development (S1), ecological development priority (S2) and urban development priority (S3). Spatial autocorrelation was used to analyze the spatial distribution of carbon storage. The results revealed a rapid urban expansion encroaching on cultivated land and forest from 2010 to 2020, resulting in a total urban area of 1957.50 km2 by 2020. Carbon storage experienced a total loss of 6.86 × 106 t, primarily between 2010 and 2015. The InVEST model indicated a spatial distribution in a pattern of “low in the middle and high around”, with areas of low carbon storage showing large-scale faceted aggregate distribution by 2035. Under different regional development scenarios, the S3 exhibited the highest carbon storage loss, reaching 150.93 × 106 t. The S1 experienced a decline of 136.30 × 106 t, while the S2 only experienced a reduction of 24.26 × 106 t. The primary driving factor of carbon storage reduction is the conversion of forest and cultivated land into urban areas. It is recommended that the implementation of regional ecological protection policies and the optimization of land use structures effectively minimize the loss of carbon storage. Full article
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13 pages, 3846 KiB  
Article
3D-STARNET: Spatial–Temporal Attention Residual Network for Robust Action Recognition
by Jun Yang, Shulong Sun, Jiayue Chen, Haizhen Xie, Yan Wang and Zenglong Yang
Appl. Sci. 2024, 14(16), 7154; https://doi.org/10.3390/app14167154 - 15 Aug 2024
Viewed by 448
Abstract
Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial–Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model [...] Read more.
Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial–Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model significantly improves the performance of action recognition through the following three main innovations: (1) the conversion from skeleton points to heat maps. Using Gaussian transform to convert skeleton point data into heat maps effectively reduces the model’s strong dependence on the original skeleton point data and enhances the stability and robustness of the data; (2) a spatiotemporal attention mechanism (STA). A novel spatiotemporal attention mechanism is proposed, focusing on the extraction of key frames and key areas within frames, which significantly enhances the model’s ability to identify behavioral patterns; (3) a multi-stage residual structure (MS-Residual). The introduction of a multi-stage residual structure improves the efficiency of data transmission in the network, solves the gradient vanishing problem in deep networks, and helps to improve the recognition efficiency of the model. Experimental results on the NTU-RGBD120 dataset show that 3D-STARNET has significantly improved the accuracy of action recognition, and the top1 accuracy of the overall network reached 96.74%. This method not only solves the robustness shortcomings of existing methods, but also improves the ability to capture spatiotemporal features, providing an efficient and widely applicable solution for action recognition based on skeletal data. Full article
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14 pages, 10752 KiB  
Article
Analysis of Surface Runoff Characteristics in Zhengzhou City under Extreme Rainfall Conditions
by Yong Wang, Shuangquan Li, Chanjuan Hu, Jie Ren, Peng Liu, Chang Zhao and Mengke Zhu
Sustainability 2024, 16(16), 6980; https://doi.org/10.3390/su16166980 - 14 Aug 2024
Viewed by 622
Abstract
In recent years, global climate change has become more and more obvious, and extreme rainfall weather has occurred frequently, which has a serious impact on people’s life and property safety. In order to reduce the risk of urban flooding and contribute to the [...] Read more.
In recent years, global climate change has become more and more obvious, and extreme rainfall weather has occurred frequently, which has a serious impact on people’s life and property safety. In order to reduce the risk of urban flooding and contribute to the sustainable development of the urban economy, society, and environment, this study takes Zhengzhou City as the study area. The surface runoff during extreme rainfall events from 2005 to 2023 was simulated using the SCS-CN model, and the spatiotemporal patterns of surface runoff during extreme rainfall conditions and their influencing factors were investigated. The results showed that (1) the average annual extreme rainfall in the study area was 95.6 mm, and the average annual surface runoff was 76.5 mm, with cultivated land contributing the most to surface runoff, accounting for more than 50%. The annual average frequency of extreme rainfall in the study area ranged from 0 to 3 times. (2) During the extreme rainfall events in 2021 and 2023, the surface runoff of the main urban area was relatively great. Under the influence of impermeable surfaces, the surface runoff of the main urban area was greater than that of the surrounding area, even when the rainfall in the main urban area was less than that in the surrounding urban area. In addition, during these two extreme rainfall events, the surface runoff in the slight slope (<5°) area was the greatest; overall, the larger the slope was, the smaller the surface runoff. (3) Differences between rainfall and surface runoff (DRS) of the different administrative districts in the study area showed three trends from 2005 to 2020, with those of most areas showing a clear decreasing trend, which was affected mainly by the surface runoff potential of the land use type. Under the same rainfall conditions (110 mm), the surface runoff of urban land and construction land was 1.4–2.5 times that of various types of woodland and grassland. From 2005 to 2020, the area of urban land and other construction land increased by 104.13%, the coverage area of woodland and grassland decreased by 35.90%, and the surface runoff potential increased in most areas of the study area. To reduce the risk of urban waterlogging, most areas of Zhengzhou, especially the main urban area and slight slope areas, need to rationally regulate land use and increase the coverage ratio of woodland and grassland. Full article
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23 pages, 7510 KiB  
Article
The Urban–Rural Transformation and Its Influencing Mechanisms on Air Pollution in the Yellow River Basin
by Chen Xu, Zhenzhen Yin, Wei Sun, Zhi Cao and Mingyang Cheng
Sustainability 2024, 16(16), 6978; https://doi.org/10.3390/su16166978 - 14 Aug 2024
Viewed by 559
Abstract
Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban–rural transition affects PM2.5 are [...] Read more.
Air pollution has recently gained much attention from the general population. Despite pollution control being an issue in both urban and rural regions, most of the available research has concentrated on urban districts. Hence, investigations into how urban–rural transition affects PM2.5 are warranted within the framework of urban–rural integration. Using the Yellow River Basin as a case study, this study employed the entropy method and Analytic Hierarchy Process (AHP) to uncover the extent of urban–rural transformation. It then used the spatial autocorrelation method to investigate the spatiotemporal features of PM2.5 and the spatial econometric model to investigate the mechanisms that influence the relationship between urban–rural transformation and PM2.5. The results are as follows: (1) The level of urban–rural transformation shows an obvious upward trend with time. The development has progressed from asymmetrical north-east and south-west elevations to a more balanced pattern of north-east, middle-east, and west-west elevations. (2) The PM2.5 concentration increased steadily, then fluctuated, and finally decreased. Notably, the general pattern has not changed much, and it is high in the east and low in the west. (3) Different subsystems of the urban–rural transformation have different impacts on air pollution at different stages. The influence of industrial transformation (IT) on PM2.5 showed an inverted “N-shaped” curve of negative–negative–changes, and the industrial structure played a leading role in the spatiotemporal evolution of PM2.5. An inverted “U-shaped” curve forms the left side of the impact of population transition (PT) on PM2.5. Land transformation (LT) has a “U-shaped” curve for its effect on PM2.5. This study provides a new perspective on the topic of PM2.5 and its connection to urban–rural integration, which is crucial to understanding the dynamics of this shift. To achieve the goal of high-quality development, this study supports regional initiatives to reduce PM2.5 emissions in the Yellow River Basin. Moreover, the results of this study can provide a reference for decision-makers in the world’s densely populated areas that suffer from serious air pollution. Full article
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12 pages, 6271 KiB  
Article
Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
by Sihan Li, Yu Sun and Pengying Wang
Appl. Sci. 2024, 14(16), 7152; https://doi.org/10.3390/app14167152 - 14 Aug 2024
Viewed by 439
Abstract
While machine learning methods have been successful in predicting air pollution, current deep learning models usually focus only on the time-based connection of air quality monitoring stations or the complex link between PM2.5 levels and explanatory factors. Due to the lack of effective [...] Read more.
While machine learning methods have been successful in predicting air pollution, current deep learning models usually focus only on the time-based connection of air quality monitoring stations or the complex link between PM2.5 levels and explanatory factors. Due to the lack of effective integration of spatial correlation, the prediction model shows poor performance in PM2.5 prediction tasks. Predicting air pollution levels accurately over a long period is difficult because of the changing levels of correlation between past pollution levels and the future. In order to address these challenges, the study introduces a Convolutional Long Short-Term Memory (ConvLSTM) network-based neural network model with multiple feature extraction for forecasting PM2.5 levels in air quality prediction. The technique is composed of three components. The model-building process of this article is as follows: Firstly, we create a complex network layout with multiple branches to capture various temporal features at different levels. Secondly, a convolutional module was introduced to enable the model to focus on identifying neighborhood units, extracting feature scales with high spatial correlation, and helping to improve the learning ability of ConvLSTM. Next, the module for spatiotemporal fusion prediction is utilized to make predictions of PM2.5 over time and space, generating fused prediction outcomes that combine characteristics from various scales. Comparative experiments were conducted. Experimental findings indicate that the proposed approach outperforms ConvLSTM in forecasting PM2.5 concentration for the following day, three days, and seven days, resulting in a lower root mean square error (RMSE). This approach excels in modeling spatiotemporal features and is well-suited for predicting PM2.5 levels in specific regions. Full article
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26 pages, 29445 KiB  
Article
Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia
by Andualem Shiferaw, Tsegaye Tadesse, Clinton Rowe and Robert Oglesby
Atmosphere 2024, 15(8), 974; https://doi.org/10.3390/atmos15080974 - 14 Aug 2024
Viewed by 555
Abstract
Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate [...] Read more.
Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast. Full article
(This article belongs to the Special Issue Climate Change and Regional Sustainability in Arid Lands)
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21 pages, 12256 KiB  
Article
Spatiotemporal Accessibility of Rail Transport Systems in the Guangdong–Hong Kong–Macao Greater Bay Area and Its Implications on Economic Equity
by Shishu Ouyang, Pengjun Zhao and Zhaoya Gong
Land 2024, 13(8), 1285; https://doi.org/10.3390/land13081285 - 14 Aug 2024
Viewed by 374
Abstract
Reducing inequality and fostering economic growth is the tenth global goal of the United Nations for sustainable development. Rail transport significantly influences spatial structures, industrial distributions, and is vital for regional economic integration. Despite its importance, the impact of rail transport on economic [...] Read more.
Reducing inequality and fostering economic growth is the tenth global goal of the United Nations for sustainable development. Rail transport significantly influences spatial structures, industrial distributions, and is vital for regional economic integration. Despite its importance, the impact of rail transport on economic equity has not been thoroughly explored in current literature. This study aims to fill this gap by evaluating the spatiotemporal characteristics of rail transport accessibility and its implications for economic equity in the Guangdong–Hong Kong–Macao Greater Bay Area. We considered high-speed, intercity, and conventional rail transport and employ three distinct indicators—door-to-door travel time, weighted average travel time, and potential accessibility—to provide a nuanced assessment of accessibility in the region. Each indicator provides a unique perspective on how accessibility affects economic equity, collectively broadening the scope of the analysis. From 1998 to 2020, the evolution of rail transport and its consequent impact on regional economic equity is scrutinized. Advanced econometric methods, namely ordinary least squares, and spatial Durbin models, are combined with the Gini coefficient and Lorenz curve for comprehensive quantitative analysis. This approach highlights the dynamic influence of rail transport development on economic equity, contributing to the sustainable urban development discourse. The results reveal that although rail transport advancements bolster connectivity and economic growth, they also exacerbate regional economic inequality. This study provides valuable insights for urban planning and policymaking by elucidating the complex relationship between rail transport accessibility and economic equity. Our findings underscore the importance of implementing balanced and inclusive transport policies that foster growth and efficiency while mitigating socioeconomic disparities. Full article
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