Waste management and water quality evaluation prediction in urban environments through advanced robust hybrid machine learning algorithms

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS Dynamics of Atmospheres and Oceans Pub Date : 2024-09-16 DOI:10.1016/j.dynatmoce.2024.101495
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Abstract

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

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通过先进的鲁棒混合机器学习算法进行城市环境中的废物管理和水质评价预测
水质管理是环境保护的一个重要方面,需要对排入地表水体的污水进行监测和监管。本研究通过独特的混合模型 ABC-DWKNN-ICA 引入了一种预测水质评价(WQE)的新方法,该模型将距离加权 K 近邻(DWKNN)算法与人工蜂群(ABC)、萤火虫算法(FA)、帝国主义竞争算法(ICA)和重力搜索算法(GSA)集成在一起。该研究利用 2018-2020 年间来自印度特兰甘纳邦的 1106 个数据点组成的综合数据集,对一系列水质参数进行了检测,包括地下水位(GWL)、氢电位(PH)、电导率(EC)等。ABC-DWKNN-ICA 模型在 WQE 预测的 Recall、Precision、Accuracy 和 F1 Score 方面都表现出了卓越的性能,在增强特征选择、提高分类准确性、对噪声和异常值的鲁棒性、降低维度以及对大型数据集的可扩展性等方面表现突出。与现有方法(包括 ABC-DWKNN、FA-DWKNN、ICA-DWKNN 和 GSA-DWKNN 等传统混合机器学习 (HML) 算法)相比,该混合模型取得了重大进步。通过重点研究 ABC-DWKNN-ICA 的能力,而不是对所有 HML 算法进行比较,该研究突出了其在水质预测方面的卓越功效,其性能指标为:Recall 值 0.83、Precision 值 0.86、Accuracy 值 0.91 和 F1 Score 值 0.86。因此,本研究填补了一项重要的研究空白,证明了该模型在环境数据分析中的价值,并为更有效地管理水资源提供了美好前景。此外,特征选择、降维、提高准确性、噪声处理和不平衡数据集管理也是所提模型的主要优势。
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来源期刊
Dynamics of Atmospheres and Oceans
Dynamics of Atmospheres and Oceans 地学-地球化学与地球物理
CiteScore
3.10
自引率
5.90%
发文量
43
审稿时长
>12 weeks
期刊介绍: Dynamics of Atmospheres and Oceans is an international journal for research related to the dynamical and physical processes governing atmospheres, oceans and climate. Authors are invited to submit articles, short contributions or scholarly reviews in the following areas: •Dynamic meteorology •Physical oceanography •Geophysical fluid dynamics •Climate variability and climate change •Atmosphere-ocean-biosphere-cryosphere interactions •Prediction and predictability •Scale interactions Papers of theoretical, computational, experimental and observational investigations are invited, particularly those that explore the fundamental nature - or bring together the interdisciplinary and multidisciplinary aspects - of dynamical and physical processes at all scales. Papers that explore air-sea interactions and the coupling between atmospheres, oceans, and other components of the climate system are particularly welcome.
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