Neural Network Model Design for Landscape Ecological Planning Assessment Based on Hierarchical Analysis

Area of Science:

• Landscape Ecology
• Urban Planning
• Artificial Intelligence

Background:

• Traditional tree species selection relies on subjective qualitative analysis.
• Landscape ecological planning requires objective and scientific methods for evaluation.
• Assessing complex ecological structures necessitates advanced computational approaches.

Purpose of the Study:

• To develop an objective and scientific method for landscape ecological planning and tree species selection.
• To analyze landscape ecological service processes and ecological spatial structures.
• To establish an accurate and efficient state assessment method for grid structures using neural networks.

Main Methods:

• Integration of Analytic Hierarchy Process (AHP) with neural networks for quantitative analysis.
• Utilizing representation, binary suitability, weighted suitability, and process models for ecological spatial structure identification.
• Developing a backpropagation (BP) neural network model trained with autoregressive model parameters and fuzzy hierarchical analysis results.

Main Results:

• The AHP algorithm with neural networks enhances objectivity in tree species selection.
• Ecological spatial structures are identified and classified based on service processes and spatial components.
• The BP neural network-based assessment method demonstrates high speed and accuracy in grid structure evaluation.

Conclusions:

• The proposed integrated approach offers a robust framework for landscape ecological planning and evaluation.
• Quantitative methods significantly improve the scientific basis of urban tree species selection.
• The BP neural network model offers a cost-effective and efficient alternative to manual testing for structural assessments.