Construction Means of Soil Microbial Synusiologic Network Based on ANN

Area of Science:

• Environmental Science
• Data Science
• Microbiology

Background:

• Data-driven approaches are crucial for modern environmental protection and synusiologic civilization.
• Traditional synusiologic planning methods suffer from excessive human interference, limiting accuracy and operability.
• Unplanned social construction can inflict unpredictable damage on the synusiologic environment.

Purpose of the Study:

• To analyze the contribution of soil nutrient data to soil fertility.
• To develop a method for constructing soil microbial synusiologic networks.
• To improve the accuracy and operability of environmental planning and guidance.

Main Methods:

• Utilized artificial neural networks (ANNs) to simulate human brain neuron functions.
• Developed a parallel distributed processing system computing DMG model.
• Applied the model to analyze soil nutrient data and construct synusiologic networks.

Main Results:

• The ANN-based algorithm demonstrated 18% better network performance compared to traditional algorithms.
• The developed model effectively acquires, stores, and processes external knowledge.
• The system responds promptly to environmental changes.

Conclusions:

• The ANN approach offers a more accurate and operable method for soil microbial synusiologic network construction.
• This technique provides a robust framework for data-driven environmental protection and planning.
• The algorithm is suitable for widespread practical application in synusiologic environmental management.