Unraveling the water quality-ecosystem nexus using Kalman filter-driven models and feature analysis under uncertainty

IF 5.9 1区地球科学 Q1 ENGINEERING, CIVIL

Journal of Hydrology Pub Date : 2025-03-19 DOI:10.1016/j.jhydrol.2025.133092

Mojtaba Poursaeid

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引用次数: 0

Abstract

Many factors impact water quality (WQ), such as climate change and population growth. Thus, the present work aims to propose an accurate and potent solution for the WQ instabilities challenge in the South Platte River in United States. The data driven model based on the machine learning model tuned with Kalman filter (KF) was considered to reduce input data noise. The least absolute shrinkage and selection operator (LASSO) algorithm were used to analyze the importance of features and select the best inputs. The US Geological Survey (USGS) archive provided the primary database related to 2023–2024, with over 38,000 samples. The random forest (RF) was combined with KF and LASSO to reduce noise and analyze the importance of features due to the high number of samples. Artificial neural network (ANN), linear regression (LR), and support vector machine (SVM) were developed to compare the accuracy of the proposed model. The proposed model had the highest coefficient of determination values, which were between 0.95 and 0.99. Modeling the indicators revealed that some WQ variations could negatively affect aquatic ecosystems.

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来源期刊

Journal of Hydrology 地学-地球科学综合

CiteScore

11.00

自引率

12.50%

发文量

1309

审稿时长

7.5 months

期刊介绍： The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.