A method, device, and storage device for predicting rangeland condition
By integrating the SARIMA and LightGBM models, the accuracy problem of grassland land state prediction was solved, achieving more accurate land state prediction and supporting grassland ecological protection and economic development.
CN117131402BActive Publication Date: 2026-06-19CHINA UNIV OF GEOSCIENCES (WUHAN)
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF GEOSCIENCES (WUHAN)
- Filing Date
- 2023-08-16
- Publication Date
- 2026-06-19
AI Technical Summary
Technical Problem
Existing technologies are insufficient to accurately predict grassland land conditions, which affects grassland ecological functions and economic development.
Method used
A fusion method based on SARIMA and LightGBM models was adopted to construct a grassland land state prediction model through data preprocessing, model building and parameter optimization. Combined with a soil moisture prediction model, the prediction accuracy was improved.
Benefits of technology
It improves the accuracy of grassland land condition prediction, helping to develop effective land protection and development strategies.
✦ Generated by Eureka AI based on patent content.
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Figure CN117131402B_ABST
Abstract
This application provides a method, device, and storage device for predicting grassland land conditions. The method includes: acquiring a dataset; preprocessing the dataset; validating the preprocessed dataset; constructing a SARIMAX-based land condition prediction model based on a stationary time series; dividing the validated dataset into a test set and a training set; constructing a soil moisture prediction model based on the LightGBM model, the test set, and the training set; fusing the land condition prediction model and the soil moisture prediction model using a weighted fusion method to determine the fusion model; acquiring test data; and determining the prediction result using the fusion model and the test data. By optimizing and fusing the two prediction models, the advantages of both models are integrated, improving the accuracy of land condition prediction.
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