Article
Computer Science, Hardware & Architecture
Chitra Paulpandi, Murukesh Chinnasamy, Shanker Nagalingam Rajendiran
Summary: The current pandemic highlights the significance and impact of air pollution on individuals. This research proposes predicting air pollution levels using deep learning techniques and achieves higher accuracy compared to previous records.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Environmental Sciences
Yuexin Fu, Zhuhua Hu, Yaochi Zhao, Mengxing Huang
Summary: A new water quality prediction method based on TCN is proposed to address the issues of traditional methods in open water environments, showing higher accuracy and lower time complexity. The TCN model can effectively predict water quality parameters with a prediction accuracy of up to 91.91% and reduce training and prediction time costs by an average of 64.92% and 7.24% respectively.
Article
Environmental Sciences
Qixian Song, Jing Zou, Min Xu, Mingyang Xi, Zhaorong Zhou
Summary: Air quality prediction is vital in preventing pollution and improving living conditions, but current methods are limited in accuracy. This study proposes a hybrid model, IJSO-LSTM, based on LSTM neural network with improved jellyfish search optimizer, to predict AQI for Chengdu. Experimental results demonstrate that IJSO-LSTM outperforms other well-known models in terms of prediction accuracy.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Bo Zhang, Guojian Zou, Dongming Qin, Qin Ni, Hongwei Mao, Maozhen Li
Summary: This study proposes a model based on deep learning technology to accurately predict the concentration of PM2.5 in the air. The model utilizes a residual neural network and a convolutional long short-term memory network to extract spatial and temporal features of pollutant concentration and meteorological data, achieving high accuracy in prediction compared to other models.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Liang Li, Yuewen Jiang, Biqing Huang
Summary: The study focuses on using a Transformer-based model to predict influenza outbreaks, showing superior performance in long-term forecasting compared to traditional AR and RNN models by using a source selection module based on curve similarity measurement to capture spatial dependency.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Environmental Sciences
Vince Paul, R. Ramesh, P. Sreeja, T. Jarin, P. S. Sujith Kumar, Sabah Ansar, Ghulam Abbas Ashraf, Sadanand Pandey, Zafar Said
Summary: Water quality analysis is crucial for water resource management and this article presents a water quality prediction model that utilizes Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM). The model shows high accuracy in predicting water quality and demonstrates improved outcomes compared to previous methods.
Article
Engineering, Environmental
Lin Peng, Huan Wu, Min Gao, Hualing Yi, Qingyu Xiong, Linda Yang, Shuiping Cheng
Summary: This study introduces a deep learning model using transfer learning framework to improve the accuracy of long-term water quality prediction in data-constrained scenarios.
Article
Environmental Sciences
Minglei Fu, Caowei Le, Tingchao Fan, Ryhor Prakapovich, Dmytro Manko, Oleh Dmytrenko, Dmytro Lande, Shamsuddin Shahid, Zaher Mundher Yaseen
Summary: This study proposed a novel hybrid prediction model combining CEEMD-Pearson with deep LSTM neural network, which can improve the accuracy of PM2.5 prediction and achieve model convergence in less computation time.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Feng Chang, Liang Ge, Siyu Li, Kunyan Wu, Yaqian Wang
Summary: This paper proposes a self-adaptive spatial-temporal network (SA-STNet) to effectively capture the spatial-temporal dependencies of air quality. The model utilizes different components in spatial and temporal dimensions to capture the correlations of air quality, and generates final prediction results by combining the outputs of different components. Experimental results demonstrate the outstanding performance of the model on real-world datasets.
CONNECTION SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Liang Ge, Kunyan Wu, Feng Chang, Aoli Zhou, Hang Li, Junling Liu
Summary: The paper proposes a general approach named DSTFN to predict air pollutant concentration, which uses data completion, region similarity calculation, and deep spatial-temporal fusion network components. Extensive experiments demonstrate that the model achieves the highest performance compared to state-of-the-art models for air quality prediction.
INTELLIGENT DATA ANALYSIS
(2021)
Article
Environmental Sciences
Shun Wu, Fengchen Fu, Lei Wang, Minhang Yang, Shi Dong, Yongqing He, Qingqing Zhang, Rong Guo
Summary: This paper proposes a method of regional temperature prediction using deep spatiotemporal networks, which demonstrates high accuracy and stability in short-term prediction. The method is able to predict the distribution and change trend of temperature in the next 3 hours and 6 hours, with better results compared to traditional models.
Review
Computer Science, Artificial Intelligence
Manjit Kaur, Dilbag Singh, Mohamed Yaseen Jabarulla, Vijay Kumar, Jusung Kang, Heung-No Lee
Summary: The growing population and industrialization have led to a significant increase in environmental pollution, especially air pollution. This has negative impacts on both the environment and human health, resulting in higher rates of illness and death. To address this urgent problem, the development of air quality prediction models has become a crucial area of research.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Environmental Sciences
Weilin Wang, Wenjing Mao, Xueli Tong, Gang Xu
Summary: Deep learning provides a promising approach for air pollution prediction. However, existing models lack effective integration of spatial correlation, resulting in poor performance in PM2.5 prediction tasks. The proposed CR-LSTM model, combining neural networks and recursive strategy, achieves better performance in longer-term prediction tasks.
Article
Engineering, Civil
Yulai Xie, Jingjing Niu, Yang Zhang, Fang Ren
Summary: The prediction of urban crowds is crucial for traffic management and studying social phenomena at the city level. This paper proposes a novel Transformer-based model for short-term and long-term crowd flow prediction, addressing challenges such as non-linear spatial-temporal dependence, periodic laws, and external factors. The model incorporates self-attention and cross-attention mechanisms for context modeling and global memory learning, and introduces auxiliary tasks for feature encoding. Experimental results demonstrate the competitiveness of the proposed model in various prediction tasks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Dewen Seng, Qiyan Zhang, Xuefeng Zhang, Guangsen Chen, Xiyuan Chen
Summary: By utilizing deep learning and supervised learning techniques, a comprehensive prediction model based on LSTM was developed for air quality indicators like PM2.5, CO, NO2, O-3, and SO2. Normalized and transformed environmental data were used to predict overall air quality in Beijing.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Construction & Building Technology
Gabriele Bernardini, Tiago Miguel Ferreira, Pilar Baquedano Julia, Rafael Ramirez Eudave, Enrico Quagliarini
Summary: This research offers a methodology for combined spatiotemporal flood risk assessment, considering hazard, physical vulnerability, user exposure, and vulnerability. It adopts a mesoscale approach and investigates indoor and outdoor users' exposure and vulnerability, using the Analytical Hierarchy Process to combine risk factors.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Ying Liu, Chunli Chu, Ruijun Zhang, Shaoqing Chen, Chao Xu, Dongliang Zhao, Chunchun Meng, Meiting Ju, Zhi Cao
Summary: This study investigates the effects of increasing road, wall, and roof albedo on mitigating the urban heat island (UHI) effect in different areas of Tianjin. The results reveal that increasing road albedo is more effective in fringe areas, while increasing wall and roof albedo is more effective in central areas. The temperature changes induced by albedo changes also show seasonal characteristics.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Xisheng Lin, Yunfei Fu, Daniel Z. Peng, Chun-Ho Liu, Mengyuan Chu, Zengshun Chen, Fan Yang, Tim K. T. Tse, Cruz Y. Li, Xinxin Feng
Summary: This study employed computational fluid dynamics and neural network models to investigate and predict pollutant dispersion in urban environments, providing valuable insights for designing effective strategies to mitigate the impacts of hazardous pollutants.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Dipanjan Nag, Arkopal Kishore Goswami
Summary: Future-oriented urban planning should continue to focus on the principles of accessible and walkable cities. The perception of people is crucial for developing better urban walking infrastructure, but current evaluation tools often neglect the "perceived" features of the walking network. This study used conjoint analysis to evaluate users' perception of link and network attributes, revealing the importance of considering both in improving the walking environment.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Yongxin Su, Tao Zhang, Mengyao Xu, Mao Tan, Yuzhou Zhang, Rui Wang, Ling Wang
Summary: This study proposes an optimization method for household integrated demand response (HIDR) by combining rough knowledge and a dueling deep Q-network (DDQN), aiming to address uncertainties in a household multi-energy system (HMES). The simulation results demonstrate that the proposed method outperforms rule-based methods and DDQN in terms of energy cost savings.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Sijia Sun, S. F. A. Batista, Monica Menendez, Yuanqing Wang, Shuang Zhang
Summary: This paper comprehensively analyzes the energy consumption characteristics of electric buses (EBs) and diesel buses (DBs) on different bus lane configurations and operational conditions. The study shows that EBs consume less energy in suburban areas when using regular lanes, while both EBs and DBs save substantial energy when operating on dedicated bus lanes in downtown areas. Notably, shared-use bus lanes have the highest energy consumption.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Shangshang Shen, Dan Yan, Xiaojie Liu
Summary: This study developed a comprehensive theoretical framework for evaluating, diagnosing, and optimizing multi-functional urban agriculture. The framework was applied in Xiamen, China to identify the obstacles that impede its coordinated development and propose optimized modes for its development. Results showed that urban agriculture in Xiamen exhibits sound social function, moderate economic function, and poor ecological function.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Oluwafemi E. Adeyeri, Akinleye H. Folorunsho, Kayode I. Ayegbusi, Vishal Bobde, Tolulope E. Adeliyi, Christopher E. Ndehedehe, Akintomide A. Akinsanola
Summary: This study examines the impact of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics on land surface temperature distribution over West Africa. The random forest model performs the best in downscaling predictands. The southern regions consistently exhibit healthy vegetation, while areas with unhealthy vegetation coincide with hot land surface temperature clusters. Positive Normalized Difference Vegetation Index trends in the Sahel highlight rainfall recovery and subsequent greening. Southwest winds cause the upwelling of cold waters, resulting in low land surface temperatures in southern West Africa. Considering LVCET factors is crucial for prioritizing greening initiatives and urban planning.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Yuchi Cao, Yan Li, Shouyun Shen, Weiwei Wang, Xiao Peng, Jiaao Chen, Jingpeng Liao, Xinyi Lv, Yifan Liu, Lehan Ma, Guodian Hu, Jinghuan Jiang, Dan Sun, Qingchu Jiang, Qiulin Liao
Summary: The study reveals significant disparities in urban green equity, with high property price areas having better access to green spaces than low property price areas. Landscape and greening have the most significant impact on urban green space differentiation.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Shaobo Sun, Kui Shan, Shengwei Wang
Summary: Economizer control is an important measure for energy savings in air-conditioning systems during moderate seasons. Humidity measurement uncertainties have a significant impact on enthalpy-based economizer control, and an uncertainty-tolerant control strategy is proposed to mitigate these effects.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Ding Mao, Peng Wang, Yi-Ping Fang, Long Ni
Summary: This study analyzes the structure, function, operation, and failure characteristics of district heating networks (DHNs) and proposes vulnerability analysis methods. The effectiveness of these methods is validated through application to a DHN in a Chinese city. The study finds that the heat source connectivity efficiency loss rate effectively characterizes topological and functional vulnerability. It also reveals that controllable DHNs have higher functional vulnerability under large area failure scenarios.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Hamid Karimi, Saeed Hasanzadeh, Hedayat Saboori
Summary: This paper presents a stochastic and cooperative approach for the operation of a cluster of interconnected multi-energy systems. The proposed model investigates the interaction among energy systems and integrates hydrogen and water systems into the overall energy structure. The model studies the performance of energy system agents in decentralized and cooperative scheduling.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Zhiyu Yan, Xiaogang Guo, Zilong Zhao, Luliang Tang
Summary: This study proposes a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on social media data. The framework utilizes deep learning modules to extract location and water depth information from text and images, and analyzes the spatio-temporal distribution characteristics. The results show that the fusion of text and image-based information can enhance the perception of flood processes.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
M. A. Pans, G. Claudio, P. C. Eames
Summary: This study simulated and optimized a speculative district heating system in an existing urban area in Loughborough, UK. The system used only renewable heat sources and thermal energy storage to address the mismatch between heat generation and demand. The study assessed the impact of long-term storage volume and charging temperature on system cost and energy efficiency.
SUSTAINABLE CITIES AND SOCIETY
(2024)
Article
Construction & Building Technology
Jianmei Zhong, Wei Zhang, Xiaoli Wang, Jinsheng Zhan, Tao Xia, Lingzhi Xie, Xiding Zeng, Kun Yang, Zhangyu Li, Ruiwen Zou, Zepu Bai, Qing Wang, Chenyang Zhang
Summary: This study aims to propose a suitable air distribution design and reduce the energy consumption of the BSL-4 laboratory. It analyzes the diffusion characteristics of aerosols, infection risk under different air distributions, and ventilation parameters. The results show that the proposed energy-saving operation strategy can reduce the energy consumption of the laboratory by 15-30%.
SUSTAINABLE CITIES AND SOCIETY
(2024)