ArticleMethodsX2021
A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach.
Article in MethodsX, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Article
- Quantitative Morphology of Polder Landscape Based on SOM Identification Model: Case Study of Typical Polders in the South of Yangtze River.Computational intelligence and neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.