Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas

To guarantee the right to move for residents in areas where public transportation is insufficient, research is needed to identify vulnerable areas and prepare measures. This paper defines the vulnerable regions of public transportation within various city types in Korea. In order to identify appropriate areas to apply the Demand Responsive Transit (DRT), the regions with vulnerability were compared with a specific city (Yangsan-si) which already the DRT system was successfully adopted. To collect monthly bus data, web-data crawling method was performed and processed with coordinating program by matching GPS coordinate. The public transportation demand was predicted for each grid cell size (100 m, 250 m, and 500 m) by different methodologies. Various data mining models based on regression were analyzed to predict bus demand of vulnerable areas. Among models, a modified model was suggested to combine Automated machine learning models for high prediction performance. The modified model outperformed other methods as 0.685 and prediction performance was appropriate at 100 m rectangle grid. Regional characters of DRT bus allocation areas were extracted by K-means clustering method and differentiate urban and suburban types. The findings of this study provide valuable insights into conditions that DRT bus stop can be installed. The urban bus stop areas located in metropolitan cities and the suburban bus stop allocation areas located in countryside. The study results can be used as policy data for the successful introduction to prevent social exclusion and improve resident welfare in the future.

A novel integrated optimization model for carbon emission prediction: A case study on the group of 20

Carbon emission is a central factor in the study of the greenhouse effect and a crucial consideration in environmental policy making. Therefore, it is essential to establish carbon emission prediction models to provide scientific guidance for leaders in implementing effective carbon reduction policies. However, existing research lacks comprehensive roadmaps that integrate both time series prediction and analysis of influencing factors. This study combines the environmental Kuznets curve (EKC) theory to classify and qualitatively analyzes research subjects based on national development patterns and levels. Considering the autocorrelated characteristics of carbon emissions and their correlation with other influencing factors, we propose an integrated carbon emission prediction model named SSA-FAGM-SVR. This model optimizes the fractional accumulation grey model (FAGM) and support vector regression (SVR) using the sparrow search algorithm (SSA), considering both time series and influencing factors. The model is subsequently applied to predict the carbon emissions of the G20 for the next 10 years. The results demonstrate that this model significantly improves prediction accuracy compared to other mainstream prediction algorithms, exhibiting strong adaptability and high accuracy.

Role of environmental ownership and associated parameters to assess green patents in technologies with environmental scanning system as a controlling factor

There is a general conception that environmental firms are more adapted to green solutions, and environmental patents are just lagging. The existing literature has paid particular attention to identifying obstacles and situational factors associated with established firms going green and has concentrated on how and why established businesses are becoming more financially viable and ecologically sustainable. In changing environment, manufacturing companies are direct contributors to environmental impacts. Increased awareness of consumers about the environment puts a handful amount of pressure on manufacturing companies to care about the environment. It also asserts unseen pressure on the financial performance of the companies. Therefore, it is time to go for green patenting of such firms while satisfying the eco-innovation and environmental scanning process. Moreover, Environmental ownership and its associated parameters keenly monitor this aspect. This paper evaluates the performance of the support vector machine (SVM/SVR) approach for estimating patents in environment-related technologies (PERT) in China from 1995 to 2021. For this work, six independent variables related to environmental ownership and environment-related technologies were selected, which include medium and high-tech exports (MHTE), green patents applicants (GPA), listed domestic companies (LDC), human capital index (HCI), self-employment (SE), and manufacturing value added in GDP (MVA). Data for dependent and independent variables were gathered from the World Bank (WB) official data bank portal. To make an initial understanding of the data basic statistical summary was computed in R programming to see the mean, minimum and maximum values in the data set. A correlation matrix plot showed the association between dependent and independent variables. SVM/SVR with radial basis function (RBF) regression was applied to see the impact of contributing parameters that influence PERT. For PERT, the model generated 0.95 R² (RMSE = 92.43). The results of the SVR showed that the association among environmental parameters is strong. With a value of 4.82, the strongest coefficient in the SVR model is PAR. This work is novel and will benefit the manufacturing sector, analysts, policymakers, environmentalists as how green patenting can boost the eco innovation and environmental ownership and scanning system with advance technologies and practices.

A dual attention-based fusion network for long- and short-term multivariate vehicle exhaust emission prediction

The increasing number of vehicles is one main cause of atmospheric environment pollution problems. Timely and accurate long- and short-term (LST) prediction of the on-road vehicle exhaust emission could contribute to atmospheric pollution prevention, public health protection, and government decision-making for environmental management. Vehicle exhaust emission has strong non-stationary and nonlinear characteristics due to the inherent randomness and imbalance nature of meteorological factors and traffic flow. Therefore accurate LST vehicle exhaust emission prediction encounters many challenges, such as the LST temporal dependencies and complicated nonlinear correlation on various emission gases, including carbon monoxide (CO), hydrocarbon (HC), and nitric oxide (NO), and external influence factors. To resolve these challenging issues, we propose a novel hybrid deep learning framework, namely Dual Attention-based Fusion Network (DAFNet), to effectively predict LST multivariate vehicle exhaust emission with the temporal convolutional network, convolutional neural network, long short term memory (LSTM)-skip based on recurrent neural network, dual attention mechanism, and autoregressive decomposition model. The proposed DAFNet consists of three major parts: 1) a nonlinear component to effectively capture the dynamic LST temporal dependency of multivariate gas by the temporal convolutional network, convolutional neural network, and LSTM-skip. Moreover, the above two networks employ an attention mechanism to model the internal relevance of the LST temporal patterns and multivariate gas, respectively. 2) a linear component to tackle the scale-insensitive problem of the neural network model by an autoregressive decomposition model. 3) the external components are taken to compensate the impact of external factors on vehicle exhaust emission by the multilayer perceptron model. Finally, the proposed DAFNet is evaluated on two real-world vehicle emission datasets in Zibo and Hefei, China. Experimental results demonstrate that the proposed DAFNet is a powerful tool to provide highly accurate prediction for LST multivariate vehicle exhaust emission in the field of vehicle environmental management.

Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems

Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.

A Hybrid Neural Network-Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

Prediction of cancer incidence rates for the European continent using machine learning models

Cancer is one of the most important and common public health problems on Earth that can occur in many different types. Treatments and precautions are aimed at minimizing the deaths caused by cancer; however, incidence rates continue to rise. Thus, it is important to analyze and estimate incidence rates to support the determination of more effective precautions. In this research, 2018 Cancer Datasheet of World Health Organization (WHO), is used and all countries on the European Continent are considered to analyze and predict the incidence rates until 2020, for Lung cancer, Breast cancer, Colorectal cancer, Prostate cancer and All types of cancer, which have highest incidence and mortality rates. Each cancer type is trained by six machine learning models namely, Linear Regression, Support Vector Regression, Decision Tree, Long-Short Term Memory neural network, Backpropagation neural network, and Radial Basis Function neural network according to gender types separately. Linear regression and support vector regression outperformed the other models with the R2 scores 0.99 and 0.98, respectively, in initial experiments, and then used for prediction of incidence rates of the considered cancer types. The ML models estimated that the maximum rise of incidence rates would be in colorectal cancer for females by 6%.

Spatiotemporal Graph Convolution Multifusion Network for Urban Vehicle Emission Prediction

Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of the spatial interactions and temporal correlations between different road segments as well as the high nonlinearity and complexity of vehicle emission variation. The existing methods solve the problem by splitting the region into standard segments or grids based on conventional deep learning methods, without considering that urban vehicle emission varies by graph-structured traffic road network and depends on many complex external environment factors. To address these issues, a spatiotemporal graph convolution multifusion network (ST-MFGCN) is proposed to leverage the graph structural properties as the inherent connectivity of road network for urban vehicle emission prediction, which can capture the vehicle emission spatiotemporal variation patterns and learn the effects of complex environmental factors. The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external factor component to divide multisource external factors into global and individual external features; and 3) a general fusion component to merge the spatiotemporal patterns and the external features as well as fit the mutation of emission measurement data by multifusion strategy. Finally, the proposed model is evaluated on the practical monitoring data of vehicle emission data in Hefei, and the results demonstrate that our proposed model can predict regional vehicle emissions effectively.

The Influence of Traffic-Related Air Pollution (TRAP) in Primary Schools and Residential Proximity to Traffic Sources on Histone H3 Level in Selected Malaysian Children

This study aimed to investigate the association between traffic-related air pollution (TRAP) exposure and histone H3 modification among school children in high-traffic (HT) and low-traffic (LT) areas in Malaysia. Respondents' background information and personal exposure to traffic sources were obtained from questionnaires distributed to randomly selected school children. Real-time monitoring instruments were used for 6-h measurements of PM₁₀, PM_2.5, PM₁, NO₂, SO₂, O₃, CO, and total volatile organic compounds (TVOC). Meanwhile, 24-h measurements of PM_2.5-bound black carbon (BC) were performed using air sampling pumps. The salivary histone H3 level was captured using an enzyme-linked immunosorbent assay (ELISA). HT schools had significantly higher PM₁₀, PM_2.5, PM₁, BC, NO₂, SO₂, O₃, CO, and TVOC than LT schools, all at p < 0.001. Children in the HT area were more likely to get higher histone H3 levels (z = -5.13). There were positive weak correlations between histone H3 level and concentrations of NO₂ (r = 0.37), CO (r = 0.36), PM₁ (r = 0.35), PM_2.5 (r = 0.34), SO₂ (r = 0.34), PM₁₀ (r = 0.33), O₃ (r = 0.33), TVOC (r = 0.25), and BC (r = 0.19). Overall, this study proposes the possible role of histone H3 modification in interpreting the effects of TRAP exposure via non-genotoxic mechanisms.

Application of Support Vector Regression and Metaheuristic Optimization Algorithms for Groundwater Potential Mapping in Gangneung-si, South Korea

The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

A Systematic Study on the Analysis of the Emission of CO, CO2 and HC for Four-Wheelers and Its Impact on the Sustainable Ecosystem

The urbanization in Delhi NCR has led to a rapid increase in the vehicle count concerning the rise in population and mobilization. The emissions from the vehicles are currently counted amongst the main sources of air pollution in Delhi. This affects the quality of air. The emission criterion of various pollutants that are emitted from vehicles is evaluated through various International models, which include various vehicles, their modes of pollutants emitted while driving and other factors that are affecting the weather. The approximate emission of pollutants such as Carbon Monoxide (CO) and/or Particulate Matter (PM), from a variety of vehicles and different fuel types, has undergone diurnal variation over the years, depending on the time of the day. This study presents the emission factor of gaseous pollutants Hydrocarbons (HC), Carbon Monoxides (CO) and Carbon Dioxide (CO2) of 181 four-wheeler cars from different companies containing different types of fuels. The measurement of gaseous pollutants is performed for Delhi, the most polluted city in India. The various facts and data were calculated and analyzed with reference to the standard values set by the national schemes of the Pollution and Environment. Based on this statistical data obtained and analyzed, the scenarios regarding future vehicle growth rate and its impact on air quality are mentioned to overcome emission problems. Therefore, it is important to develop and deploy methods for obtaining real-world measurements of vehicle emissions, to estimate the pollutants. The analysis shows that few parameters need to be a concern for reducing the pollutants emission by vehicles. These major parameters are the high survival rates, decrease in annual mileage and major enforcement for three-to-five-year-old vehicles. This study shows that many old vehicles are used in different regions of the country, regardless of many notifications of banning old vehicles by the Government of India. These old vehicles are the major source of vehicle pollutants. The analysis stated that the diesel engine would emit less CO2/km than a petrol engine if having an almost similar engine capacity.

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

Methodology for determining ecological benefits of advanced tolling systems

Toll plazas are places on the road network where increased emissions of exhaust gases occur due to changes in vehicle driving regime in their corresponding impact areas. Therefore, they provide a great potential in terms of the ability to significantly reduce the emission of pollutants by using advanced technologies. In light of this, this paper aims at getting the most accurate quantification of pollutant emission (CO, CO2, HC and NOx) for the various vehicle categories which use Manuel System (MS), Electronic toll collection (ETC) with mechanical barriers and Multi Lane Free Flow (MLFF) system, for determining ecological benefits that can be achieved using advanced tolling systems. The measurement of the emission of harmful gases was carried out in real field conditions for the five most common classes of passenger cars, light truck and semi-trailer-truck. Vehicle speed, fuel consumption and emission of pollutants were recorded every second, in various driving processes in the impact areas of toll plazas, as well as in numerous scenarios that involve a different number of vehicles in a queue. The obtained results show that the use of the MLFF system, compared to the MS, can achieve a reduction in CO2 in the range of 25%-45% and the reduction in NOx in the range of 32%-98%, depending on the type of vehicle and the considered scenario. The case study of the tolling system in the Republic of Serbia, on a sample of 77,408,112 vehicles, has shown that moving from the existing to an advanced MLFF tolling system allows for annual ecological benefits ranging from 1,349,862 € to 1,491,391 €.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation. Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .

Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system

This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.