Environmental modeling of landscape aesthetic value in natural urban parks using artificial neural network technique

Jahani, Ali; Allahverdi, Shaghayegh; Saffariha, Maryam; Alitavoli, Asal; Ghiyasi, Samira

doi:10.1007/s40808-020-01068-2

Cited by 31 publications

(11 citation statements)

References 39 publications

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“…The aesthetic value of landscape for human well-being has a special importance not only in public perception but also in socio-environmental research [ 5 , 6 , 7 ]. According to studies [ 8 , 9 , 10 ], the aesthetic quality of urban parks landscape is in cultural aspects and the natural elements of parks, including plants and water. The combination of plants and water in urban parks influences the aesthetic quality of the landscape.…”

Section: Methods Detailsmentioning

confidence: 99%

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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%

“…We followed aesthetic perception as [19] defines “the scenery is beautiful enough to attract people to see” and [8] and [10] applied that in aesthetic landscape assessment. We used the landscape aesthetic quality scores achieved by one question for aesthetic perception.…”

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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“… 2021 ), influence of factors on the perception of the landscape (Jahani et al. 2022 ), and uncertainty quantification for water resources applications (Ciriello et al. 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables

Reveco-Quiroz

Sandoval-Díaz

Álvares

2022

Stoch Environ Res Risk Assess

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Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for extracting personal information regarding this phenomenon. However, statistical modeling for these surveys is not straightforward, as the response variable is often not explicit. Hence, we propose a set of methodological procedures to deal with pro-environmental behavior data. First, validity evidence through a factorial analysis. Second, indexes are created from factor scores, where one of the latent factors summarizes a target variable. Third, a Beta regression is used to model the index of interest. Fourth, the inferential process is performed from a Bayesian perspective, in which posterior probabilities are used to sort and select the relevant variables. Finally, suitable models are obtained, and conclusions can be drawn from them. As a motivation, we used data from two Chilean surveys to illustrate our methodology as well as interpret and discuss the results.

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“…In view of the uncorrected spirometric results which generally caused by failure to complete the test depend on the skills of operator and ability of the investigated subject to complete the test, hence, efforts are being made to use the Artificial Neural networks (ANNs) techniques to classify pulmonary function 4 . ANNs have been widely used for a great number of medical purposes such as epidemic research, diagnosis, and detection of damage as they have more predictive power 10‐12 . González‐Crespo et al proposed three methods of data mining from a small dataset of coronavirus outbreak.…”

Section: Introductionmentioning

confidence: 99%

“…4 ANNs have been widely used for a great number of medical purposes such as epidemic research, diagnosis, and detection of damage as they have more predictive power. [10][11][12] González-Crespo et al proposed three methods of data mining from a small dataset of coronavirus outbreak. The results showed that a polynomial neural network with corrective feedback as a new algorithm is useful to make an acceptable forecast with relatively the lowest prediction error for COVID-19.…”

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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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Environmental modeling of landscape aesthetic value in natural urban parks using artificial neural network technique

Cited by 31 publications

References 39 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

Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables

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

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