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Planning model of a low-carbon landscape garden environment based on PSO-BP

Author

Listed:
  • Lin Han
  • Peng Li
  • Yuting Ruan

Abstract

A particle swarm optimization-back propagation neural network (PSO-BP) is proposed. First, we collect and preprocess the planning data for low-carbon landscape environment to ensure accuracy and consistency of data; then we propose the PSO-BP model that combines the global optimization characteristics of particle swarm algorithm and the nonlinear mapping capability of the backpropagation neural network. Empirical studies show that this model can effectively reduce energy consumption and carbon emissions, thus improving the ecological environment quality, improve the ecological service function, and effectively promote the sustainable development of low-carbon landscape buildings.

Suggested Citation

  • Lin Han & Peng Li & Yuting Ruan, 2025. "Planning model of a low-carbon landscape garden environment based on PSO-BP," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 137-156.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:137-56.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae286
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