Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer.

Autor: Khosravi K; Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran., Barzegar R; Department of Bioresource Engineering, McGill University, Montreal, Canada; Department of Geography & Environmental Studies, Wilfrid Laurier University, Waterloo, Canada., Golkarian A; Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran., Busico G; Department of Geology, Lab. of Engineering Geology & Hydrogeology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania 'Luigi Vanvitelli', Via Vivaldi 43, Caserta 81100, Italy. Electronic address: gianluigi.busico@unicampania.it., Cuoco E; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania 'Luigi Vanvitelli', Via Vivaldi 43, Caserta 81100, Italy., Mastrocicco M; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania 'Luigi Vanvitelli', Via Vivaldi 43, Caserta 81100, Italy., Colombani N; Department of Materials, Environmental Sciences and Urban Planning, Polytechnic University of Marche, Via Brecce Bianche 12, 60131, Italy., Tedesco D; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania 'Luigi Vanvitelli', Via Vivaldi 43, Caserta 81100, Italy., Ntona MM; Department of Geology, Lab. of Engineering Geology & Hydrogeology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania 'Luigi Vanvitelli', Via Vivaldi 43, Caserta 81100, Italy., Kazakis N; Department of Geology, Lab. of Engineering Geology & Hydrogeology, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece. Electronic address: kazakis@geo.auth.gr.
Jazyk: angličtina
Zdroj: Journal of contaminant hydrology [J Contam Hydrol] 2021 Oct; Vol. 242, pp. 103849. Date of Electronic Publication: 2021 Jun 12.
DOI: 10.1016/j.jconhyd.2021.103849
Abstrakt: Trace element (TE) pollution in groundwater resources is one of the major concerns in both developing and developed countries as it can directly affect human health. Arsenic (As), Barium (Ba), and Rubidium (Rb) can be considered as TEs naturally present in groundwater due to water-rock interactions in Campania Plain (CP) aquifers, in South Italy. Their concentration could be predicted via some readily available input variables using an algorithm like the iterative classifier optimizer (ICO) for regression, and novel hybrid algorithms with additive regression (AR-ICO), attribute selected classifier (ASC-ICO) and bagging (BA-ICO). In this regard, 244 groundwater samples were collected from water wells within the CP and analyzed with respect to the electrical conductivity, pH, major ions and selected TEs. To develop the models, the available dataset was divided randomly into two subsets for model training (70% of the dataset) and evaluation (30% of the dataset), respectively. Based on the correlation coefficient (r), different input variables combinations were constructed to find the most effective one. Each model's performance was evaluated using common statistical and visual metrics. Results indicated that the prediction of As and Ba concentrations strongly depends on HCO 3 - , while Na + is the most effective variable on Rb prediction. Also, the findings showed that the most powerful predictive models were those that used all the available input variables. According to models' performance evaluation metrics, the hybrid ASC-ICO outperformed other hybrid (BA- and AR-ICO) and standalone (ICO) algorithms to predict As and Ba concentrations, while both hybrid ASC- and BA-ICO models had higher accuracy and lower error than other algorithms for Rb prediction.
(Copyright © 2021 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: