Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning Models for Predicting the Ammonium Concentration in Alluvial Groundwaters
https://orcid.org/https://orcid.org/0000-0003-2045-001X
https://orcid.org/https://orcid.org/0000-0002-8296-7885
https://orcid.org/https://orcid.org/0000-0001-9731-1304
Considering the great importance of groundwater quality for water supply, in the last decade, significant scientific attention has been devoted to nitrate reduction transformation pathways and nitrogen conservation in groundwaters in the form of ammonium. To evaluate and assess the ability of machine learning models to predict the ammonium concentration, four machine learning models were applied: a three-layer neural network (NN), a deep neural network (DNN), and two variants of support vector regression (SVR) models: with linear and with Gaussian radial basis function kernel. A dataset of 322 samples with 13 predictor variables representing selected parameters responsible for oxidative/reductive nitrogen transformations in shallow alluvial groundwater was acquired from measurements in 55 monitoring wells during a 6-year monitoring period (2011–2016) in Serbia. Applied principal component analysis and cluster analysis gave an insight into conditionality and relations between the selected parameters, distinguishing four main factors, which explained 70.97% of total variance, and classifying examined objects by similarity. Extracted factors correlated the concentration patterns, implying the main nitrogen transformations in examined groundwater. The machine learning models were successfully applied for predicting the ammonium concentration with high determination coefficients (R2) in tests: 0.84 for DNN and 0.64 for NN, while the SVR did not prove to be adequate with the best R2 of 0.24.
Machine Learning Models for Predicting the Ammonium Concentration in Alluvial Groundwaters
https://orcid.org/https://orcid.org/0000-0003-2045-001X
https://orcid.org/https://orcid.org/0000-0002-8296-7885
https://orcid.org/https://orcid.org/0000-0001-9731-1304
Considering the great importance of groundwater quality for water supply, in the last decade, significant scientific attention has been devoted to nitrate reduction transformation pathways and nitrogen conservation in groundwaters in the form of ammonium. To evaluate and assess the ability of machine learning models to predict the ammonium concentration, four machine learning models were applied: a three-layer neural network (NN), a deep neural network (DNN), and two variants of support vector regression (SVR) models: with linear and with Gaussian radial basis function kernel. A dataset of 322 samples with 13 predictor variables representing selected parameters responsible for oxidative/reductive nitrogen transformations in shallow alluvial groundwater was acquired from measurements in 55 monitoring wells during a 6-year monitoring period (2011–2016) in Serbia. Applied principal component analysis and cluster analysis gave an insight into conditionality and relations between the selected parameters, distinguishing four main factors, which explained 70.97% of total variance, and classifying examined objects by similarity. Extracted factors correlated the concentration patterns, implying the main nitrogen transformations in examined groundwater. The machine learning models were successfully applied for predicting the ammonium concentration with high determination coefficients (R2) in tests: 0.84 for DNN and 0.64 for NN, while the SVR did not prove to be adequate with the best R2 of 0.24.
Machine Learning Models for Predicting the Ammonium Concentration in Alluvial Groundwaters
https://orcid.org/https://orcid.org/0000-0003-2045-001X
https://orcid.org/https://orcid.org/0000-0002-8296-7885
https://orcid.org/https://orcid.org/0000-0001-9731-1304
Considering the great importance of groundwater quality for water supply, in the last decade, significant scientific attention has been devoted to nitrate reduction transformation pathways and nitrogen conservation in groundwaters in the form of ammonium. To evaluate and assess the ability of machine learning models to predict the ammonium concentration, four machine learning models were applied: a three-layer neural network (NN), a deep neural network (DNN), and two variants of support vector regression (SVR) models: with linear and with Gaussian radial basis function kernel. A dataset of 322 samples with 13 predictor variables representing selected parameters responsible for oxidative/reductive nitrogen transformations in shallow alluvial groundwater was acquired from measurements in 55 monitoring wells during a 6-year monitoring period (2011–2016) in Serbia. Applied principal component analysis and cluster analysis gave an insight into conditionality and relations between the selected parameters, distinguishing four main factors, which explained 70.97% of total variance, and classifying examined objects by similarity. Extracted factors correlated the concentration patterns, implying the main nitrogen transformations in examined groundwater. The machine learning models were successfully applied for predicting the ammonium concentration with high determination coefficients (R2) in tests: 0.84 for DNN and 0.64 for NN, while the SVR did not prove to be adequate with the best R2 of 0.24.
Machine Learning Models for Predicting the Ammonium Concentration in Alluvial Groundwaters
Environ Model Assess
Perović, Marija (Autor:in) / Šenk, Ivana (Autor:in) / Tarjan, Laslo (Autor:in) / Obradović, Vesna (Autor:in) / Dimkić, Milan (Autor:in)
Environmental Modeling & Assessment ; 26 ; 187-203
01.04.2021
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Machine Learning Models for Predicting the Ammonium Concentration in Alluvial Groundwaters
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| Elsevier | 1982
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