Estimation of Water Quality Parameters Using an Ensemble Learning Model Optimized with Levy Flight and Sparrow Search Algorithms

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.