Forest Environmental Carrying Capacity Based on Deep Learning.

Area of Science:

• Environmental Science
• Forestry
• Artificial Intelligence

Background:

• Forest environmental carrying capacity is crucial for sustainable development.
• Accurate assessment and prediction are needed for effective forest management.
• Existing models may not fully capture complex temporal dynamics or handle missing data.

Purpose of the Study:

• To propose a novel deep learning-based assessment system for forest environmental carrying capacity.
• To evaluate and predict forest environmental carrying capacity in 40 cities in China's Yangtze River Delta.
• To dynamically assess forest environmental carrying capacity scores for 34 Chinese provinces and cities from 2015-2020.

Main Methods:

• Development of a deep learning model integrating unidirectional and bidirectional Long Short-Term Memory (LSTM) networks.
• Utilizing a Mask layer to effectively handle missing data, enhancing evaluation robustness.
• Application of the model to assess forest environmental carrying capacity across diverse geographical regions in China.

Main Results:

• Comprehensive evaluation and prediction of forest environmental carrying capacity for 40 cities in the Yangtze River Delta.
• Dynamic evaluation of forest environmental carrying capacity scores for 34 provinces and cities (2015-2020).
• Demonstrated improvement in prediction accuracy and evaluation results due to the integrated LSTM and Mask layer.

Conclusions:

• The proposed deep learning model offers a robust and accurate method for assessing forest environmental carrying capacity.
• The model's ability to handle time series correlations and missing data enhances its applicability in dynamic environmental assessments.
• Findings provide valuable insights for sustainable forest management and policy-making in China.