Analysis of Landscape Ecological Planning Based on the High-Order Multiwavelet Neural Network Algorithm

Area of Science:

• Landscape ecology
• Artificial intelligence
• Environmental science

Background:

• Rapid industrialization post-Industrial Revolution has damaged ecosystems and landscape resources.
• Effective landscape ecological evaluation and planning are crucial for environmental protection.
• Traditional methods may lack the efficiency and accuracy needed for modern ecological challenges.

Purpose of the Study:

• To propose a novel high-order wavelet neural network (WNN) algorithm for landscape ecological evaluation.
• To develop a model for assessing landscape ecology and informing ecological planning.
• To compare the efficiency and accuracy of the WNN model against traditional neural networks.

Main Methods:

• Integration of wavelet analysis and artificial neural networks to create a high-order WNN algorithm.
• Development of a landscape ecological evaluation model utilizing the proposed WNN algorithm.
• Comparative analysis of WNN performance against Backpropagation (BP) neural networks.

Main Results:

• The WNN algorithm achieved target accuracy in 3600 fewer training iterations than the BP neural network.
• The WNN model demonstrated low Mean Squared Error (MSE) of 0.0639 and Mean Absolute Error (MAE) of 0.1501.
• The model achieved 98.67% accuracy in evaluating landscape land resource sustainability with an average error of 0.005.

Conclusions:

• The WNN-based model offers a highly effective and accurate method for landscape ecological evaluation.
• This approach provides a valuable decision-making basis for practical landscape ecological planning.
• The WNN algorithm presents a significant advancement in computational efficiency for ecological assessments.