SUSPENDED SEDIMENT TRANSPORT ESTIMATION BY X-RAY FLUORESCENCE ANALYSIS

• BEITIA Carlos

  Kitami Institute of Technology

• NAKAYAMA Keisuke

  Dept. of Civil Engineering, Kobe University

• MARUYA Yasuyuki

  Dept. of Civil and Earth Resources Engineering, Kyoto University

• OHTSU Naofumi

  Instrumental Analysis Center, Kitami Institute of Technology

• YAMASAKI Shintaro

  Dept. of Civil and Environmental Engineering, Kitami Institute of Technology

• YAMANE Misao

  Non-Technical Division, Kitami Institute of Technology

• Alex S. J. WYATT

  Atmosphere and Ocean Research Institute, The University of Tokyo

• KOMAI Katsuaki

  Dept. of Civil and Environmental Engineering, Kitami Institute of Technology

Abstract

 This study proposes an improvement to the method for evaluating suspended sediment (SS) transport from different regions of a catchment to the downstream area using X-ray fluorescence (XRF) analysis. The target area, the Oromushi River basin in Hokkaido, has SS yields among the highest in Japan due to the predominance of soils originated from volcanic rock and high contents of silt. The geochemical composition of soil and deposited sediment samples collected from 12 land groups, each with an area of 2.45 km², was analyzed by XRF analysis. Kruskal-Wallis H test results pointed out that among the detected geochemical components by XRF only Na₂O, SiO₂, Al₂O₃, CaO, Fe₂O₃, MgO, P₂O₅, K₂O, MnO and TiO₂ were statistically suitable to conduct SS estimations. A modified Neural Network Analysis (NNA) was applied to estimate the SS yields from the different land groups. The results suggested that land groups G1, G3, G11 and G12 representing 33.45% of the total area produce about 64.70% of the total SS yield.

Journal

• Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)

  Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 72 (4), I_1039-I_1044, 2016

  Japan Society of Civil Engineers

Keywords

• X-ray fluorescence analysis
• water quality
• geochemical composition
• neural network model

Details

• CRID: 1390282680330364800
• NII Article ID: 130005311936
• NII Book ID: AN10426673
• ISSN: 18808751
• ISSN: 2185467X
• ISSN: 09167374
• DOI: 10.2208/jscejhe.72.i_1039
• NDL BIB ID: 027186153
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I027186153
• Web Site: https://www.jstage.jst.go.jp/article/jscejhe/72/4/72_I_1039/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed