人民珠江2024，Vol.45Issue(4)：98-106,9.DOI:10.3969/j.issn.1001-9235.2024.04.012
基于PCA-GA-XGBoost模型的吉林省水资源承载力评价
Carrying Capacity Evaluation on Water Resources of Jilin Province Based on PCA-GA-XGboost Model
摘要
Abstract
To improve the efficiency and accuracy of carrying capacity evaluation in water resources,this paper proposes an indicator evaluation model based on principal component analysis(PC A),genetic algorithm(GA),and eXtreme gradient boosting tree(XGBoost).Meanwhile,fourteen evaluation indicators are defined with water resources,socio-economics,and ecological environment employed as subsystems.PC A is adopted to reduce the dimensionality of the evaluation indicators.Additionally,based on XGBoost,this paper conducts an evaluation analysis on the carrying capacity of water resources from 2011 to 2021 and utilizes GA to optimize four parameters in XGBoost.The results show that after simplifying the evaluation indicators by PCA,the correlation coefficient of the PCA-GA-XGBoost model is better than GA-B,GA-SVM,GA-XGBoost,and XGBoost.The carrying capacity of water resources in Jilin Province from 2011 to 2021 is between 0.192 and 0.724,presenting a trend of first increasing,then decreasing,and finally increasing with improved carrying capacity situation.Meanwhile,the built-in function of eigenvalue importance ranking in the model is leveraged to conclude the fact that the indicator with the largest importance is identified as the applied fertilizer amount per hectare(0.530 7).关键词
主成分分析/遗传算法/极限梯度提升树/水资源承载力/吉林省Key words
principal component analysis/genetic algorithm/extreme gradient boosting tree/carrying capacity of water resources/Jilin Province分类
建筑与水利引用本文复制引用
庞博文,李治军..基于PCA-GA-XGBoost模型的吉林省水资源承载力评价[J].人民珠江,2024,45(4):98-106,9.基金项目
国家科技支撑计划项目(2014BAD12B01-03) （2014BAD12B01-03）
