China Institute of Water Resources and Hydropower Research

Guangquan Liu

Liu Liu

Yingfei Bai

Qiang Zhang

Yan Gao

Zhimin Zhang

Wanli Shi

Pengfei Du

Heng Fu

Puhang Li

Yonggui Chen

Xiaoning Tu

Ning Ai

Jian Hou

Chunyu Qi

Disclosed is a method for controlling soil and water loss in a wind-water complex erosion region, which includes: randomly selecting a data acquisition reference point in a historical control area and acquiring a soil sample; detecting soil data of different soil layers on the soil sample, including soil viscosity, moisture content and organic matter content; selecting a plant sample, calculating a solid-binding coefficient of the plant sample and selecting an optimal soil-binding plant; fitting a relationship model; acquiring a control soil sample in a target control area of the wind-water complex erosion region, calculating a flatness and a soil and water loss coefficient u; setting a soil and water loss coefficient threshold u; and setting a planting density and optimal soil-binding plant type in the target control area. The present invention achieves the optimal soil and water loss control effect by considering soil-binding effects of different plants from multiple aspects.

Method for controlling soil and water loss in wind-water complex erosion region

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.

Method, system and equipment for vegetation restoration or rehabilitation based on machine learning

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Beijing, China

Guangquan Liu