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Mokarram M, Taripanah F, Pham TM. Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:122886-122905. [PMID: 37979107 DOI: 10.1007/s11356-023-30859-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.
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Affiliation(s)
- Marzieh Mokarram
- Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran
| | - Farideh Taripanah
- Department of Desert Control and Management, University of Kashan, Kashan, Iran
| | - Tam Minh Pham
- Research Group On "Fuzzy Set Theory and Optimal Decision-Making Model in Economics and Management", Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
- VNU School of Interdisciplinary Studies, Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
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Ruiz de Gauna DE, Sánchez LE, Ruiz-Iniesta A. Design of a pollution ontology-based event generation framework for the dynamic application of traffic restrictions. PeerJ Comput Sci 2023; 9:e1534. [PMID: 37705667 PMCID: PMC10495943 DOI: 10.7717/peerj-cs.1534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/21/2023] [Indexed: 09/15/2023]
Abstract
The environmental damage caused by air pollution has recently become the focus of city council policies. The concept of the green city has emerged as an urban solution by which to confront environmental challenges worldwide and is founded on air pollution levels that have increased meaningfully as a result of traffic in urban areas. Local governments are attempting to meet environmental challenges by developing public traffic policies such as air pollution protocols. However, several problems must still be solved, such as the need to link smart cars to these pollution protocols in order to find more optimal routes. We have, therefore, attempted to address this problem by conducting a study of local policies in the city of Madrid (Spain) with the aim of determining the importance of the vehicle routing problem (VRP), and the need to optimise a set of routes for a fleet. The results of this study have allowed us to propose a framework with which to dynamically implement traffic constraints. This framework consists of three main layers: the data layer, the prediction layer and the event generation layer. With regard to the data layer, a dataset has been generated from traffic data concerning the city of Madrid, and deep learning techniques have then been applied to this data. The results obtained show that there are interdependencies between several factors, such as weather conditions, air quality and the local event calendar, which have an impact on drivers' behaviour. These interdependencies have allowed the development of an ontological model, together with an event generation system that can anticipate changes and dynamically restructure traffic restrictions in order to obtain a more efficient traffic system. This system has been validated using real data from the city of Madrid.
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Xu S, Li W, Zhu Y, Xu A. A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks. Sci Rep 2022; 12:14434. [PMID: 36002466 PMCID: PMC9402967 DOI: 10.1038/s41598-022-17754-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/30/2022] [Indexed: 12/03/2022] Open
Abstract
In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and effectively improve environmental air quality, the target of our research was cities with serious air pollution problems to establish a model for air pollution prediction. We used the daily monitoring data of air pollution from January 2016 to December 2020 for the respective cities. We used the long short term memory networks (LSTM) algorithm model to solve the problem of gradient explosion in recurrent neural networks, then used the particle swarm optimization algorithm to determine the parameters of the CNN-LSTM model, and finally introduced the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) decomposition to decompose air pollution and improve the accuracy of model prediction. The experimental results show that compared with a single LSTM model, the CEEMDAN-CNN-LSTM model has higher accuracy and lower prediction errors. The CEEMDAN-CNN-LSTM model enables a more precise prediction of air pollution, and may thus be useful for sustainable management and the control of air pollution.
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Affiliation(s)
- Shenyi Xu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China
| | - Wei Li
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China
| | - Yuhan Zhu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China.,Collaborative Innovation Center of Statistical Data Engineering, Technology & Application, Zhejiang Gongshang University, Hangzhou, China
| | - Aiting Xu
- School of Statistics and Mathematics, Zhejiang Gongshang University, No.18 Xuezheng Street, Xiasha Higher Education Park, Hangzhou, Zhejiang, China. .,Collaborative Innovation Center of Statistical Data Engineering, Technology & Application, Zhejiang Gongshang University, Hangzhou, China.
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Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods. SENSORS 2022; 22:s22124485. [PMID: 35746265 PMCID: PMC9227396 DOI: 10.3390/s22124485] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/04/2022] [Accepted: 06/10/2022] [Indexed: 02/01/2023]
Abstract
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.
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Babu Saheer L, Bhasy A, Maktabdar M, Zarrin J. Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas. Front Big Data 2022; 5:822573. [PMID: 35402904 PMCID: PMC8993228 DOI: 10.3389/fdata.2022.822573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/26/2022] [Indexed: 11/13/2022] Open
Abstract
Monitoring, predicting, and controlling the air quality in urban areas is one of the effective solutions for tackling the climate change problem. Leveraging the availability of big data in different domains like pollutant concentration, urban traffic, aerial imagery of terrains and vegetation, and weather conditions can aid in understanding the interactions between these factors and building a reliable air quality prediction model. This research proposes a novel cost-effective and efficient air quality modeling framework including all these factors employing state-of-the-art artificial intelligence techniques. The framework also includes a novel deep learning-based vegetation detection system using aerial images. The pilot study conducted in the UK city of Cambridge using the proposed framework investigates various predictive models ranging from statistical to machine learning and deep recurrent neural network models. This framework opens up possibilities of broadening air quality modeling and prediction to other domains like vegetation or green space planning or green traffic routing for sustainable urban cities. The research is mainly focused on extracting strong pieces of evidence which could be useful in proposing better policies around climate change.
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Affiliation(s)
- Lakshmi Babu Saheer
- Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
| | - Ajay Bhasy
- Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
| | - Mahdi Maktabdar
- Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
| | - Javad Zarrin
- Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
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Popa CL, Dobrescu TG, Silvestru CI, Firulescu AC, Popescu CA, Cotet CE. Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities. SENSORS 2021; 21:s21217329. [PMID: 34770634 PMCID: PMC8586941 DOI: 10.3390/s21217329] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 02/01/2023]
Abstract
Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.
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Zhang D, Wei B. Smart Sensors and Devices in Artificial Intelligence. SENSORS 2020; 20:s20205945. [PMID: 33096695 PMCID: PMC7588986 DOI: 10.3390/s20205945] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 10/20/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Dan Zhang
- Department of Mechanical Engineering, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: ; Tel.: +1-647-209-0959
| | - Bin Wei
- Department of Computer Science, Algoma University, Sault Ste Marie, ON P6A 2G4, Canada;
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Pop MD, Proștean O, David TM, Proștean G. Hybrid Solution Combining Kalman Filtering with Takagi-Sugeno Fuzzy Inference System for Online Car-Following Model Calibration. SENSORS 2020; 20:s20195539. [PMID: 32992622 PMCID: PMC7582673 DOI: 10.3390/s20195539] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/23/2020] [Accepted: 09/24/2020] [Indexed: 11/25/2022]
Abstract
Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.
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Affiliation(s)
- Mădălin-Dorin Pop
- Automation and Applied Informatics Department, Politehnica University of Timisoara, Bvd. V. Parvan, No. 2, 300223 Timisoara, Romania;
| | - Octavian Proștean
- Automation and Applied Informatics Department, Politehnica University of Timisoara, Bvd. V. Parvan, No. 2, 300223 Timisoara, Romania;
- Correspondence:
| | - Tudor-Mihai David
- Faculty of Automation and Computers, Politehnica University of Timisoara, Bvd. V. Parvan, No. 2, 300223 Timisoara, Romania;
| | - Gabriela Proștean
- Management Department, Politehnica University of Timisoara, Bvd. M. Viteazu, No. 1, 300223 Timisoara, Romania;
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