Abstract: The field of landscape architecture is constantly evolving to address sustainability and climate change. There is a rising chance to use these technology into landscape design as renewable energy sources become more prevalent. An effective technique for evaluating the possibility of incorporating renewable energy management into landscape architecture is currently required. As a result, decision-making procedures are now manual and subjective, requiring greater precision and consistency. Deep learning algorithms can be used to examine the possibilities for renewable energy management in landscape architecture, which would help to solve this problem. Deep learning is a branch of artificial intelligence that automatically extracts complicated relationships and patterns from data using multi-layer neural networks. With inputs like topography, solar radiation, and climate, the algorithm can determine where in a particular landscape renewable energy installations would be most effective.

YaWei Wu and Xiang Meng, “Analysis of the Application and Potential of Renewable Energy in Landscape Architecture” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160388

@article{Wu2025,
title = {Analysis of the Application and Potential of Renewable Energy in Landscape Architecture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160388},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160388},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {YaWei Wu and Xiang Meng}
}