Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

Li, Tao; Sun, Guihua; Yang, Chen; Kai, Li; Ma, Shuhua; Huang, Lei

doi:10.1016/j.scitotenv.2018.02.163

Cited by 107 publications

(37 citation statements)

References 58 publications

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“…• Table S7 • Table S8 • Table S9 • Table S12 • Table S13 • Table S15 • Table S16 The investigation of biogeochemical processes, such as desorption of metals, P-and N-release from sediments to hypolimnion and algal growth, and water quality of reservoirs usually requires the measurement of many variables and usually yields a huge amount of complex data. All these data combined are often difficult to interpret because of their complexity on spatial and temporal scales (Filik Iscen et al, 2008;Li et al, 2018). To counteract this problem, artificial neural networks have attracted increased interest for water quality assessment (Kalteh et al, 2008).…”

Section: 1029/2019wr025991mentioning

confidence: 99%

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Melo

Gontijo

Frascareli

et al. 2019

Water Resources Research

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Water quality in reservoirs is often compromised in many regions worldwide by nutrients and trace metals. This demands continuous monitoring; however, analyses of large data sets collected during regular monitoring remain a difficult task. Multivariate techniques offer a fast and robust approach for interpreting complex results. The objective of this study was to check the efficacy of selforganizing maps (SOMs) as a tool to investigate biogeochemical processes. This tool can also help to illustrate influences of land use patterns on the water quality of reservoirs. Here we use the Itupararanga Reservoir in Brazil as a subtropical example. Vertical profiles were sampled from seven sites in the reservoir in a total of seven campaigns over 24 months. Next to physicochemical parameters in the water column (dissolved oxygen, Eh, pH, and temperature), levels of nutrients (NO 3 − , NH 4 + , and PO 4 3− ), transition and trace metals (Al, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, and Zn), and chlorophyll-a (Chla) were measured. These variables were correlated with land use using SOM. With this technique samples were classified into 17 distinct groups that showed distinct influences of spatial heterogeneity and seasonality. The analyses helped to reveal a seasonal stratification period, where Fe, Mn, and P were released from sediments. Nutrients and some metal inputs (Al and Fe) were related to agricultural, urban, and grass/pasture areas around the reservoir. Our approach also helped to explain physical and biogeochemical seasonality in the reservoir.

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Section: 1029/2019wr025991mentioning

confidence: 99%

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Melo

Gontijo

Frascareli

et al. 2019

Water Resources Research

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“…Some studies evaluated its superiority compared to established methods for EDA, such as the widely adopted principal component analysis (PCA), particularly when applied to large data sets (e.g., Astel et al, 2007; Iseri et al, 2009; Karafistan & Gemikonakli, 2020; Reusch et al, 2005). For water‐related studies, SOM has been successfully used for water quality classification (Li et al, 2018) or for interpreting biogeochemical processes by correlating multiple variables in complex environments (Melo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling

Libera

Pedretti

Tateo

et al. 2020

Water Resources Research

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This work proposed a novel method to elucidate the controls of As mobility in complex aquifers based on an unsupervised machine learning algorithm, Self-Organizing Map (SOM), and process-based geochemical modeling. The approach is tested in the shallow aquifers of the Venetian Alluvial Plain (VAP) near Venice, Italy, where As concentrations seasonally and locally exceed recommended drinking water limits. SOM was fed using information from two geochemical surveys on eight VAP boreholes, and continuous reading of hourly groundwater head levels and weekly geochemical analyses from three VAP boreholes between mid-October 2017 and end of January 2018. The SOM analysis is consistent with redox-controlled dissolution-precipitation hydrous ferric oxides (HFOs) as a key control of As mobility in the aquifer. Dissolved As is positively correlated to Fe and NH þ 4 and negatively to the oxidizing-reducing potential (ORP). Negative correlation between As and groundwater head levels suggests a redox control by rainfall-driven recharge, which adds oxidants to the aquifer while progressively attenuating As. This mechanism is tested using process-based geochemical modeling, which simulates different transport modalities of oxidants entering the aquifer. Starting from reducing aquifer conditions, the model reproduces correctly the observed ORP and the trends in As and Fe, when the function describing the occurrence of oxidizing events scales according to the temporal occurrence of rainfall events. Heterogeneity can strongly control the local-scale effectiveness of recharge as a natural As attenuating factor, requiring a different model analysis to be properly assessed and to be developed in a follow-up study.

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“…Among the variables, the most relevant ones for the study were selected: farmer’s academic level; main features and crop type of agricultural holdings; irrigation and fertilization techniques; farmers’ opinions regarding measures to improve the aquifer state and encouraging the use of desalinated seawater for irrigation. This kind of analysis is well known in environmental and agricultural studies [58,59,60].…”

Section: Methodsmentioning

confidence: 99%

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

Aznar-Sánchez

Ureña

Velasco-Muñoz

et al. 2019

IJERPH

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In the Campo de Níjar (southeast Spain), an intensive horticulture model under plastic has been developed based on the use of groundwater. For many years, aquifers have been overexploited, almost generating an environmental collapse. The construction of a desalination plant was planned to improve this situation and to achieve sustainable aquifer management. However, the aquifer is still being overexploited, since farmers scarcely use desalinated seawater for irrigation. In this paper, farmers irrigating with desalinated seawater are characterized, since they contribute to aquifer sustainability. The study aimed to identify the variables which condition the use of this water resource, as well as the kinds of incentives that encourage this option. For this purpose, a survey was undertaken within a sample of 110 farmers. A cluster analysis and a binary logistic regression were employed. The results from the cluster analysis allowed the characterization of farmers who use desalinated seawater for irrigation. Furthermore, the regression model showed the variables that determine a more intensive use of this irrigation source, such as crop diversification, availability of different water resources and the conductivity of aquifer water available for irrigation. The incentives promoting the use of desalinated seawater for irrigation that most encourage farmers are the implementation of tax relief, price reductions and the obligation to install rainwater collection systems.

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Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

Cited by 107 publications

References 58 publications

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling

Aquifer Sustainability and the Use of Desalinated Seawater for Greenhouse Irrigation in the Campo de Níjar, Southeast Spain

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