GIS-based non-grain cultivated land susceptibility prediction using data mining methods

GIS-based non-grain cultivated land susceptibility prediction using data mining methods

Abstract The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) metaheuristic algorithm. In order to, a total of 184 NCL areas were identifi...

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Bibliographic Details
Main Authors: Qili Hao, Tingyu Zhang, Xiaohui Cheng, Peng He, Xiankui Zhu, Yao Chen
Format: Article
Language:English
Published: Nature Portfolio 2024-02-01
Series:Scientific Reports
Subjects:
Metaheuristic algorithms
Particle swarm optimization
Optimized extreme gradient boosting
Environmental management
Online Access:https://doi.org/10.1038/s41598-024-55002-y

Internet

https://doi.org/10.1038/s41598-024-55002-y

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