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

Yu, Zhi Qiang; Amano, Hiroki; Nakagawa, Kei; Berndtsson, Ronny

doi:10.1007/s12665-018-7816-5

Cited by 14 publications

(3 citation statements)

References 28 publications

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“…SOM is a powerful and effective tool for complex data analyses such as data mining, estimation, and prediction. Using SOM, informative reference vectors are obtained via iterative updates under three main successive procedures: competition with nodes (1), selection of a winner node (2), and updating of the reference vector (3) (Yu et al, 2018). Every node has its vector adjusted according to sequential algorithm with the Gaussian neighbourhood function.…”

Section: Kohonen Self-organisation Map (K-som)mentioning

confidence: 99%

Pan evaporation is increased by submerged macrophytes

Simon-Gáspár

Soós

Anda

2022

Hydrol. Earth Syst. Sci.

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Abstract. The topic of evaporation estimates is fundamental to land-surface hydrology. In this study, FAO-56 Penman–Monteith equation (FAO56–PM), multiple stepwise regression (MLR), and Kohonen self-organising map (K–SOM) techniques were used for the estimation of daily pan evaporation (Ep) in three treatments, where C was the standard class A pan with top water, S was a pan with sediment covered bottom, and SM was class A pan containing submerged macrophytes (Myriophyllum spicatum, Potamogeton perfoliatus, and Najas marina), at Keszthely, Hungary, in a six-season experiment, between 2015 and 2020. The modelling approach included six measured meteorological variables. Average Ep varied from 0.6 to 6.9 mm d−1 for C, 0.7 to 7.9 mm d−1 for S, and from 0.9 to 8.2 mm d−1 for SM during the growing seasons studied. Correlation analysis and K–SOM visual representation revealed that air temperature and global radiation had positive correlation, while relative humidity had a negative correlation with the Ep of C, S, and SM. The results showed that the MLR method provided close compliance (R2=0.58–0.62) with the observed pan evaporation values, but the K–SOM method (R2=0.97–0.98) yielded by far the closest match to observed evaporation estimates for all three pans. To our best knowledge, no similar work has been published previously using the three modelling methods for seeded pan evaporation estimation. The current study differs from previous evaporation estimates by using neural networks even with those pans containing sediments and submerged macrophytes. Their evaporation will be treated directly by K–SOM, in which the modelling is more than the simple Ep of a class A pan filled with clean tap water.

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Section: Kohonen Self-organisation Map (K-som)mentioning

confidence: 99%

Pan evaporation is increased by submerged macrophytes

Simon-Gáspár

Soós

Anda

2022

Hydrol. Earth Syst. Sci.

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“…Titration using 0.1N HCl determined HCO3 − . Charge balance errors (CBE) were established for all groundwater samples according to [27]:…”

Section: Sampling and Analysismentioning

confidence: 99%

Spatial Characteristics of Groundwater Chemistry in Unzen, Nagasaki, Japan

Nakagawa

Amano

Berndtsson

2021

Water

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Nitrate pollution in groundwater is a serious problem in Shimabara Peninsula, Nagasaki, Japan. A better understanding of the hydrogeochemical evolution of groundwater in vulnerable aquifers is important for health and environment. In this study, groundwater samples were collected at 12 residential and 57 municipal water supply wells and springs in July and August 2018. Nitrate (NO3−N) concentration at eight sampling sites (12%) exceeded Japanese drinking water standard for NO3 + NO2−N (10 mg L−1). The highest nitrate concentration was 19.9 mg L−1. Polluted groundwater is distributed in northeastern, northwestern, and southwestern areas, where land is used for intensive agriculture. Correlation analysis suggests that nitrate sources are agricultural fertilizers and livestock waste. Dominant groundwater chemistry is (Ca+Mg)−HCO3 or (Ca+Mg)−(SO4+NO3) type. Groundwater with higher nitrate concentration is of (Ca+Mg)−(SO4+NO3) type, indicating nitrate pollution affecting water chemistry. Principal component analysis extracted two important factors controlling water chemistry. The first principal component explained dissolved ions through water–rock interaction and agricultural activities. The second principal component explained cation exchange and dominant agricultural effects from fertilizers. Hierarchical cluster analysis classified groundwater into four groups. One of these is related to the dissolution of major ions. The other three represent nitrate pollution.

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“…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]. By running the SOM, informative reference vectors are extracted by iterative updates under three important steps, that is competition with nodes, selection of winner node, and updating of the reference vectors.…”

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

Cited by 14 publications

References 28 publications

Pan evaporation is increased by submerged macrophytes

Pan evaporation is increased by submerged macrophytes

Spatial Characteristics of Groundwater Chemistry in Unzen, Nagasaki, Japan

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

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