Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams, Seyedeh Reyhaneh; Jahani, Ali; Kalantary, Saba; Moeinaddini, Mazaher; Khorasani, Nematollah

doi:10.1038/s41598-021-81455-6

Cited by 57 publications

(33 citation statements)

References 43 publications

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“…The MLP was prepared by [32] in the field of computer science. Multi-Layer Perceptron (MLP) neural network is one of the most accurate models in environmental modelling researches [ 20 , 22 , 23 , 24 , 25 ] and we used this technique to model landscape score as the aesthetic quality of landscape in our research.…”

Section: Methods Detailsmentioning

confidence: 99%

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

Aboufazeli

Jahani²,

Farahpour

2021

MethodsX

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This work presents a simplified method for the application of the Multi-Layer Perceptron (MLP) model that aims to predict the aesthetic quality of the landscape designed by water and plants in different forms and volume. The MLP was prepared by (Rosenblat) in the field of computer science, followed by the application of a MLP in landscape aesthetic quality prediction proposed by (Jahani). In the method of this research, the structure of MLP was structured for aesthetic quality prediction of plants and water in urban park landscapes. The accuracies of designed MLP structures were tested to achieve the most accurate one in aesthetic quality prediction. This method creates an environmental decision support system tool for landscape designers, and it is a platform to predict the quality of environment. In practice, the designed environmental decision support system tool is applied by landscape managers to predict the aesthetic quality of landscape in designing new urban parks. Applies Multi-Layer Perceptron method in landscape assessment. Accurate MATLAB extension for landscape aesthetic evaluation. Defined criteria for aesthetic value of landscape.

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Section: Methods Detailsmentioning

confidence: 99%

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

Aboufazeli

Jahani²,

Farahpour

2021

MethodsX

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“…The structure of feed‐forward and back‐propagation neural networks designed in MATLAB program (R 2016b package). Next, performance evaluation of the ANN model was done by several math indexes such as mean squared error – MSE (Equation )), root mean squared error – RMSE (Equation )), mean absolute error – MAE (Equation )) 28‐30

MSE = \frac{\sum_{i = 1}^{n} {(y_{i} - {\overset{true^}{y}}_{i})}^{2}}{n}

RMSE = \sqrt{\frac{\sum_{i = 1}^{n} {false(y_{i} - {\overset{y}{true}}_{i} false)}^{2}}{n}}

MAE = \frac{\sum_{i = 1}^{n} | y_{i} - {\overset{y}{true}}_{i} |}{n}

…”

Section: Methodsmentioning

confidence: 99%

“…Next, performance evaluation of the ANN model was done by several math indexes such as mean squared error -MSE (Equation ( 1)), root mean squared error -RMSE (Equation ( 2)), mean absolute error -MAE (Equation ( 3)). [28][29][30] MSE =…”

Section: Model Selectionmentioning

confidence: 99%

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Kalantary

Pourbabaki

Jahani³

et al. 2021

Concurrency and Computation

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The spirometry is considered a preclinical tool for the evaluation of the respiratory system. The formal lung volumes measurement and health status lung system are made using spirometry. Artificial neural network (ANN) has been introduced in solving complex problems in a large number of different settings, including medical diagnosis support system as predictive power. An objective of this research was intended to investigate the development of a new decision support system (DSS) using ANN modeling approaches and algorithms to predict pulmonary function in people. The spirometry data and general characteristics, anthropometric data, and body composition parameters (N = 130) were obtained from subjects. The classification of pulmonary function was performed by the multi-layer perceptron (MLP) model. Findings show that the MLP model is capable of classifying respiratory abnormalities in different people. The ANN model was totally 93.6%, 92.3%, 84.6%, and 91.5% successful in correctly classified in training, validation, test, and all data, respectively. Also, a DSS tool was created that allows the evaluation and classification of the results of spirometry data. It appears that ANNs are useful in classification pulmonary function.

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“…Among the statistical models, linear regression analysis showed promise because of its reasonable accuracy and relatively simple implementation when compared to other methods [36]. Multi-linear regression (MLR) analysis is a means to evaluate the relationship between independent and dependent factors [37]. As such, this model has been widely used to analyze the relationship between urban environmental physico-chemical parameters and LST.…”

Section: Multivariate Linear Regressionmentioning

confidence: 99%

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

et al. 2021

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This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.

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Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Cited by 57 publications

References 43 publications

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

Development of a decision support system tool to predict the pulmonary function using artificial neural network approach

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

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