Ecological environment quality prediction method based on time sequence remote sensing image and residual error reconstruction

Through a multi-regression model based on time-series remote sensing images, combining climate factors and human activity factors, the shortcomings of the interaction between human activities and natural factors in the prediction of ecological environment quality in the mining area are solved, and accurate prediction of the ecological environment quality of the mining area is achieved, and sustainable development of the mining area is supported.

CN119721332AActive Publication Date: 2025-03-28CHINA UNIV OF MINING & TECH (BEIJING) +1
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Patent Information

Application Number
CN202411747209.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-29
Publication Date
2025-03-28
Estimated Expiration
2044-11-29

AI Technical Summary

Technical Problem

It is difficult for the existing technology to accurately consider the interaction between human activities and natural factors in the prediction of ecological environment quality of mining areas, resulting in insufficient forecasting of future environmental change trends and affecting the sustainable development of mining areas.

Method used

Using a method based on time-series remote sensing image and residual reconstruction, a multivariate regression model is constructed, and regression training is performed using climate factors and human activity factors respectively to obtain a comprehensive ecological environment quality prediction function model.

Benefits of technology

It has achieved accurate prediction of the ecological environment quality of the mining area, can comprehensively consider natural factors and the impact of human activities, improve the ability to capture environmental change trends, and supports scientific and reasonable ecological protection and reclamation planning.

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Abstract

The invention discloses an ecological environment quality prediction method and model based on a time sequence remote sensing image and residual reconstruction, and the method comprises the steps: S1, obtaining an ecological environment quality sample data set, and segmenting the ecological environment quality sample data set into a data subset only affected by climate factors and a data subset jointly affected by climate factors and human activities; s2, adding the trained regression function A and regression function B in a grid map by a multiple regression model to form a comprehensive ecological environment quality prediction function model; and S3, collecting climatic factor data and human activity data in the research mining area, inputting the climatic factor data and the human activity data into the multiple regression model, and outputting an ecological environment quality prediction result. According to the method, residual error reconstruction is carried out by utilizing the ecological environment quality sample data set, the regression function A and the regression function B are respectively trained, the comprehensive ecological environment quality prediction function model comprehensively considering natural factors and human activities is constructed, and future mining area ecological environment quality prediction is effectively realized.
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