<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to scientifically allocate rural landscape resources, reasonably plan rural tourism space, and ensure that the local characteristics of the countryside are not homogenized when carrying out rural landscape design, this paper studies rural landscape design strategies based on deep learning models. The extreme learning machine algorithm, DBN-RBM algorithm model and the improved DBN-DELM algorithm are the main technical means to obtain research data and parameter calibration results for tourism planning and development work, and the rural planning direction and planning theme is determined through the rural landscape design pre-analysis work. The data show that the main motives of tourists’ rural experience tourism are close to nature 85.90% and leisure vacation 75%, followed by understanding culture 45.30%, novelty 30.70%, parent-child education 29.20%, health retreat 30.40%, and business meeting 5.90%. In this paper, the study of rural landscape planning and design can effectively alleviate the contradiction between people’s production and living and ecological environment and coordinate the benign development of rural and tourism elements in their respective spaces.</p>
</abstract>

In order to scientifically allocate rural landscape resources, reasonably plan rural tourism space, and ensure that the local characteristics of the countryside are not homogenized when carrying out rural landscape design, this paper studies rural landscape design strategies based on deep learning models. The extreme learning machine algorithm, DBN-RBM algorithm model and the improved DBN-DELM algorithm are the main technical means to obtain research data and parameter calibration results for tourism planning and development work, and the rural planning direction and planning theme is determined through the rural landscape design pre-analysis work. The data show that the main motives of tourists’ rural experience tourism are close to nature 85.90% and leisure vacation 75%, followed by understanding culture 45.30%, novelty 30.70%, parent-child education 29.20%, health retreat 30.40%, and business meeting 5.90%. In this paper, the study of rural landscape planning and design can effectively alleviate the contradiction between people’s production and living and ecological environment and coordinate the benign development of rural and tourism elements in their respective spaces.

Rural landscape design strategy based on deep learning model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Rural landscape design strategy based on deep learning model"}

In order to scientifically allocate rural landscape resources, reasonably plan rural tourism space, and ensure that the local characteristics of the...

Rural landscape design strategy based on deep learning model

Data publikacji: 02 cze 2023

Zakres stron: -

Otrzymano: 15 maj 2022

Przyjęty: 10 paź 2022

DOI: https://doi.org/10.2478/amns.2023.1.00137

Słowa kluczowe
Deep learning model, Rural landscape design, Tourism planning and development, Landscape shaping

© 2023 Yida Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Rural landscape design strategy based on deep learning model

Data publikacji: 02 cze 2023

Zakres stron: -

Otrzymano: 15 maj 2022

Przyjęty: 10 paź 2022

DOI: https://doi.org/10.2478/amns.2023.1.00137

Słowa kluczoweDeep learning model, Rural landscape design, Tourism planning and development, Landscape shaping

© 2023 Yida Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning model, Rural landscape design, Tourism planning and development, Landscape shaping