Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain

Choubin, Bahram; Abdolshahnejad, Mahsa; Moradi, Ehsan; Querol, Xavier; Mosavi, Amir; Shamshirband, Shahaboddin; Ghamisi, Pedram

doi:10.1016/j.scitotenv.2019.134474

Cited by 109 publications

(53 citation statements)

References 92 publications

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“…This method allows researchers in the field of architecture and urbanization to analyze the relationship between spatial configurations and social and behavioral structure of space and recognize, and analyze the effect of changes in urban networks on citizen mentality and behavior [43,46]. Stedman believed that space is regarded as the initial and main core of the pattern of social and behavioral events and a basis for social and cultural activities [47,48]. Spatial order is defined as the pattern of space syntax and their mutual relationship.…”

Section: Space Syntaxmentioning

confidence: 99%

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Fathi

Sajadzadeh

Sheshkal

et al. 2020

IJERPH

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Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.

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Section: Space Syntaxmentioning

confidence: 99%

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Fathi

Sajadzadeh

Sheshkal

et al. 2020

IJERPH

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“…There is no straightforward guideline for splitting the training and testing data in machine learning modeling [38][39][40][41][42][43][44][45][46]. For instance, the study of Choubin [47] used a total of 63% of their data for model development, whereas Qasem et al, [48] utilized 67% of data, Asadi et al, [41], Samadianfard et al, [49,50], and Dodangeh et al, [51] used 70%, and Zounemat-Kermani et al, [52] implemented 80% of total data to develop their models. Thus, to develop the studied models for PE estimation, we divided the data into training (70%) and testing (30%).…”

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

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Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

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“…In this study, the dataset containing 105 pavement segments is used, where, approximately 70% of the data (i.e., 75 segments) are used for training, and There has been no standard method for splitting training and testing data. For instance, Choubin et al [70] used a total of 63% of their data for model development, whereas Shamshirband et al [71] utilized 67% of data, Mohammadzadeh et al [72] 70%, and Samadarianfard et al [73] implemented 80% of total data to develop their models. In this study, the dataset containing 105 pavement segments is used, where, approximately 70% of the data (i.e., 75 segments) are used for training, and the remaining 30 segments are utilized for testing.…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

et al. 2019

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Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

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Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain

Cited by 109 publications

References 92 publications

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement

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