同济大学学报（自然科学版）2025，Vol.53Issue(3)：450-461,12.DOI:10.11908/j.issn.0253-374x.23264
基于Levy飞行和麻雀搜索算法优化集成学习模型的水质估算
Estimation of Water Quality Parameters Using an Ensemble Learning Model Optimized with Levy Flight and Sparrow Search Algorithms
摘要
Abstract
Due to the optical complexity of water bodies and the interactions among various water quality parameters,utilizing ensemble machine learning methods for estimating water quality parameters offers advantages.However,selecting hyperparameters in the modeling process remains challenging.The sparrow search algorithm(SSA)can rapidly search for optimal parameters of ensemble machine learning models,while the Levy flight algorithm prevents SSA from being trapped in local optima,thereby improving the accuracy and efficiency of the model.In this paper,the Levy flight algorithm and SSA were used to optimize three ensemble learning models:random forest(RF),AdaBoost regression(ABR),and CatBoost regression(CBR).Taking Zhengzhou Dongfeng Canal and Xiong'er River as the study area,estimation models(LSSA-RF,LSSA-ABR,and LSSA-CBR)were developed based on measured chlorophyll-a and total suspended solids concentrations.The experimental results show that after optimization,various indicators show improvements to varying degrees.Among them,the LSSA-CBR model exhibits the best performance.The CBR model,which is modeled under the gradient boosting framework,demonstrates higher learning capability compared to RF and ABR models.For the estimation of chlorophyll-a,the root mean square error(RMSE)of the LSSA-CBR estimation model is 2.325 μg·L-1,and the coefficient of determination(R2)is 0.896.For the estimation of total suspended solids,the RMSE of the LSSA-CBR model is 1.598 mg·L-1,and R2 is 0.882.Finally,the LSSA-CBR model,demonstrating strong accuracy,was applied to Planet images to evaluate the spatial distribution of chlorophyll-a and total suspended solids in rivers,providing a valuable reference for quickly understanding the distribution of urban river water quality and conducting water quality assessment and management.关键词
叶绿素a/总悬浮物/集成学习模型/Levy飞行—麻雀搜索算法/城市河流Key words
chlorophyll a/total suspended matter/integrated learning model/Levy flight-sparrow search algorithm/urban river分类
信息技术与安全科学引用本文复制引用
李爱民,康轩,袁铮,王海隆,闫翔宇,许有成..基于Levy飞行和麻雀搜索算法优化集成学习模型的水质估算[J].同济大学学报（自然科学版）,2025,53(3):450-461,12.基金项目
河南省自然科学基金面上项目(242300421372) （242300421372）
河南省高等学校重点科研项目(24B170010) （24B170010）
