Classification of groundwater chemistry in Shimabara, using self-organizing maps

Nakagawa, Kei; Amano, Hiroki; Kawamura, Akira; Berndtsson, Ronny

doi:10.2166/nh.2016.072

Cited by 29 publications

(7 citation statements)

References 32 publications

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“…The optimal number of nodes in an SOM can be selected by using the heuristic rule m = 5 √ n, where m denotes the total number of nodes and n is the number of samples in the data set [40,43]. The ratio of the number of rows to the number of columns in the feature map is determined by calculating the square root of the ratio of the largest eigenvalue of the correlation matrix of the input data to the second-largest eigenvalue [19]. In this study, the eigenvalues were obtained using principal component analysis of the input dataset.…”

Section: Sommentioning

confidence: 99%

“…However, these multivariate analyses are generally based on linear principles and they cannot overcome difficulties arising from biases due to the complexity and nonlinearity of the datasets and from inherent correlations between variables [16]. Therefore, a self-organizing map (SOM) approach, a neural network-based pattern analysis with unsupervised learning, has recently been applied to map changes in groundwater levels and chemistry in space and time (e.g., [17][18][19][20]). An SOM, which can cluster a set of hydrochemical data into two or more independent groups, is superior to other statistical tools because it can: (1) deal with system nonlinearities, (2) be developed from data without requiring mechanistic knowledge of the system, (3) handle noisy or irregular data and be easily and quickly updated, and (4) be used to interpret and visualize information of multiple variables or parameters [16,21].…”

Section: Introductionmentioning

confidence: 99%

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Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

Tsuchihara

Shirahata

Ishida

et al. 2020

Water

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Paddy rice fields on an alluvial fan not only use groundwater for irrigation but also play an important role as groundwater recharge sources. In this study, we investigated the spatial distribution of isotopic and hydrochemical compositions of groundwater in the Nasunogahara alluvial fan in Japan and applied a self-organizing map (SOM) to characterize the groundwater. The SOM assisted with the hydrochemical and isotopic interpretation of the groundwater in the fan, and clearly classified the groundwater into four groups reflecting the different origins. Two groundwater groups with lower isotopic ratios of water than the mean precipitation values in the fan were influenced by the infiltration of river water flowing from higher areas in the catchments and were differentiated from each other by their Na+ and Cl− concentrations. A groundwater group with higher isotopic ratios was influenced by the infiltration of paddy irrigation water that had experienced evaporative isotopic enrichment. Groundwater in the fourth group, which was distributed in the upstream area of the fan where dairy farms dominated, showed little influence of recharge waters from paddy rice fields. The findings of this study will contribute to proper management of the groundwater resources in the fan.

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Section: Sommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

Tsuchihara

Shirahata

Ishida

et al. 2020

Water

View full text Add to dashboard Cite

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“…SOM are also known as Kohonen maps, a type of unsupervised artificial neural network [42][43][44]. Due to their powerful data processing capability, SOM has been extensively applied in data mining, classification, and prediction in many subjects [45][46][47][48][49][50]. The main advantage of SOM is that it can project high-dimensional, complicated input data into low-dimensional array (usually two-dimensional) and simplified visualized maps based on data similarity principles [2,[51][52][53].…”

Section: Self-organizing Maps (Som)mentioning

confidence: 99%

“…These procedures were performed using a modified version of SOM Toolbox 2.0 [24,58]. Further details on the SOM methodology can be found in [46,47,58].…”

Section: Self-organizing Maps (Som)mentioning

confidence: 99%

Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply

Nakagawa

Shimada

et al. 2020

Minerals

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The 2016 Kumamoto earthquake had a significant impact on groundwater levels and quality. In some areas, the groundwater level increased significantly due to the release of groundwater from upstream mountainous regions. Conversely, the groundwater level in other areas greatly decreased due to the creation of new fracture networks by the earthquake. There were also significant changes in certain groundwater quality variables. In this study, we used clustering based SOM (self-organizing maps) analysis to improve the understanding of earthquake effects on groundwater quality. We were especially interested in effects on groundwater used for drinking purposes and in nitrate concentration. For this purpose, we studied groundwater nitrate (NO3− + NO2−–N) concentrations for the period 2012–2017. Nitrate concentration changes were classified into seven typical SOM clusters. The clusters were distributed in three representative geographical regions: a high concentration region (>4 mg/L), a low concentration region (<1.6 mg/L) with minimal anthropogenic loading area, and an intermediate concentration region (2–4 mg/L). Depending on these regions, the nitrate concentration changes just before and after the earthquake had both increasing and decreasing trends between 2015–2017. This points to complex physiographical relationships for release of stored upstream groundwater, promotion of infiltration of shallow soil water/groundwater, and nitrate concentration as affected by earthquakes. We present an analysis of these complex relationships and a discussion of causes of nitrate concentration changes due to earthquakes.

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“…Nguyen et al, 2015;Nakagawa et al, 2017). It has been used in different research 122 fields such as hydrology(Kalteh and Berndtsson, 2007), wastewater treatment(Yu et al, 2014), and 123 meteorology(Nishiyama et al, 2007).…”

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confidence: 99%

Hydrogeochemical evolution of groundwater in a Quaternary sediment and Cretaceous sandstone unconfined aquifer in Northwestern China

Amano

Nakagawa

et al. 2018

Environ Earth Sci

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A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is 18 important for the protection of water resources. To assess groundwater chemistry, groundwater 19 sampling was performed from different representative aquifers in 2012-13. A Piper trilinear diagram 20 showed that the groundwater types can be classified into Na-SO4 and Na-Cl types. Only one 21 groundwater sample was Na-HCO3 type. The dominant cations for all samples were Na +. However, 22 2 the dominant anions varied from HCO3to SO4 2-, and as well Cl-. The mean total dissolved solid 23 (TDS) content of groundwater in the region was 1,889 mg/L. Thus, only 20% of groundwater 24 samples meet Chinese drinking water standards (<1,000 mg/L). Principal component analysis (PCA) combined with hierarchical cluster analysis (HCA) and self-organizing maps (SOM) were 26 applied for the classification of the groundwater geochemistry. The three first principal components 27 explained 58, 20, and 16% of the variance, respectively. The first component reflects sulfate 28 minerals (gypsum, anhydrite) and halite dissolution, and/or evaporation in the shallow aquifer. The 29 second and third components are interpreted as carbonate rock dissolution. The reason for two 30 factors is that the different aquifers give rise to different degree of hydrogeochemical evolution 31 (different travel distances and travel times). Identified clusters for evolution characteristic and 32 influencing factors were confirmed by the PCA-HCA methods. Using information from eight ion 33 components and SOM, formation mechanisms and influencing factors for the present groundwater 34 quality were determined.

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Classification of groundwater chemistry in Shimabara, using self-organizing maps

Cited by 29 publications

References 32 publications

Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identification of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply

Hydrogeochemical evolution of groundwater in a Quaternary sediment and Cretaceous sandstone unconfined aquifer in Northwestern China

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