Machine Learning Vegetation Selection Model for Loess Plateau Restoration
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Solution Overview
Problem
Current vegetation restoration methods in the Loess Plateau face challenges such as low survival and preservation rates of afforested trees due to harsh conditions and improper tree species selection, leading to ineffective ecological benefits and high labor costs.
Innovation Solution
A method and system utilizing machine learning to simulate a natural ecosystem by acquiring historical growth environment data, extracting site and growth condition features, and training a vegetation community structure selection model to optimize tree species selection based on current conditions, thereby improving screening accuracy and ecological restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual discussion and single site condition-based vegetation species selection are used, then labor cost is high and screening accuracy is low
Solution Approach 1:
The patent replaces manual discussion and single-condition selection methods with a machine learning-based vegetation community structure selection model. The model automatically processes multiple environmental factors (precipitation, temperature, soil properties, topography) to screen optimal vegetation species, substituting human manual work with an automated computational system that provides both high accuracy and reduced labor costs.
2Reliability
If improper tree species selection is made, then survival rate and preservation rate are low
Solution Approach 1:
The patent implements preliminary action by using the vegetation community structure selection model to screen and select the most suitable vegetation species before actual restoration activities. The model analyzes historical environmental data and current site conditions to predict which species will have the highest survival and preservation rates, allowing planners to make informed decisions ahead of time and avoid improper species selection.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring environmental factors and comparing actual vegetation growth outcomes with model predictions. This feedback loop allows the model to refine its species selection accuracy over time, improving survival and preservation rates through iterative optimization based on real-world performance data.
3Quantity of substance
If forest coverage is increased without proper species selection, then forest ecosystem stability is poor
Solution Approach 1:
The patent applies local quality by matching specific vegetation species to specific local environmental conditions through the selection model. Instead of uniformly planting the same species across all areas, the model identifies the optimal species for each location based on local precipitation, temperature, soil, and topography characteristics. This ensures that forest coverage expansion maintains ecosystem stability by placing appropriate species in appropriate locations.
Data Source
AI summary
A method for vegetation restoration or rehabilitation of simulating a natural ecosystem based on machine learning (ML) includes: acquiring historical growth environment data of a vegetation community; extracting a site condition feature and a growth condition feature of each vegetation species; restoring and rehabilitating a vegetation community structure selection model; and selecting, based on the vegetation community structure selection model, an optimal vegetation species according to current growth environment data of the vegetation community, and restoring and rehabilitating a vegetation community simulating a natural ecosystem. The method comprehensively considers various factors affecting vegetation restoration based on the site condition and the growth condition. The method has the advantages of simple operation, fast modeling speed, high calculation efficiency, and high screening accuracy and can realize accurate and effective vegetation restoration to improve the quality of the ecological environment.