Pollution of Russian Northern Seas with Heavy Metals: Comparison of Atmospheric Flux and River Flow

Виноградова, А. А.; Котова, Е. И.

doi:10.1134/s0001433819070119

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…For instance, on an observed date, the ANN model predicts a 70.1% contribution from UMR and a 29.5% contribution from CR, while IEMMA suggests a greater disparity with 84% from UMR and only 16% from CR (Figure 7). These variances illustrate the different perspectives through which ANN and IEMMA view the river's flow dynamics-ANN through hydrological data patterns and IEMMA through nuanced isotopic signatures reflecting water mixing and source contributions [67,68].…”

Section: Comparative Analysis For River Flow Componentsmentioning

confidence: 95%

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Chomcheawchan,

Pawana,

Julphunthong

et al. 2024

Preprint

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This study innovatively assesses the Mun River flow components in Thailand, integrating Artificial Neural Networks (ANN) and Isotopic (18O) End-Member Mixing Analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. ANN predicts the UMR and CR contribute approximately 70.1% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings underscore the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

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Section: Comparative Analysis For River Flow Componentsmentioning

confidence: 95%

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Chomcheawchan,

Pawana,

Julphunthong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, on an observed date, the ANN model predicts a 70.5% contribution from the UMR and a 29.5% contribution from the CR, while IEMMA suggests a greater disparity with 84% from the UMR and only 16% from the CR (Figure 9). These variances illustrate the different perspectives through which ANN and IEMMA view the river's flow dynamics: ANN through hydrological data patterns and IEMMA through isotopic signatures reflecting water mixing and source contributions [70,71].…”

Section: Comparative Analysis For River Flow Componentsmentioning

confidence: 95%

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Chomcheawchan,

Pawana,

Julphunthong

et al. 2024

Geosciences

View full text Add to dashboard Cite

This study innovatively assesses the Mun River flow components in Thailand, integrating artificial neural networks (ANNs) and isotopic (δ18O) end-member mixing analysis (IEMMA). It quantifies the contributions of the Upper Mun River (UMR) and Chi River (CR) to the overall flow, revealing a discrepancy in their estimated contributions. The ANN method predicts that the UMR and CR contribute approximately 70.5% and 29.5% respectively, while IEMMA indicates a more pronounced disparity with 84% from UMR and 16% from CR. This divergence highlights the distinct perspectives of ANN, focusing on hydrological data patterns, and IEMMA, emphasizing isotopic signatures. Despite discrepancies, both methods validate UMR as a significant contributor to the overall flow, highlighting their utility in hydrological research. The findings emphasize the complexity of river systems and advocate for an integrated approach of river flow analysis for a comprehensive understanding, crucial for effective water resource management and planning.

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Impacts of Oil and Mineral Extraction

Elias

2021

Threats to the Arctic

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Pollution of Russian Northern Seas with Heavy Metals: Comparison of Atmospheric Flux and River Flow

Cited by 7 publications

References 10 publications

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Innovative Assessment of Mun River Flow Components through ANN and Isotopic End-Member Mixing Analysis

Impacts of Oil and Mineral Extraction

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