Application of Response Surface Methodology and Machine Learning Combined with Data Simulation to Metal Determination of Freshwater Sediment-Reference-Cited by-同舟云学术

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Application of Response Surface Methodology and Machine Learning Combined with Data Simulation to Metal Determination of Freshwater Sediment

Published:2017-09 Issue:9 Volume:228 Page:
ISSN:0049-6979
Container-title:Water, Air, & Soil Pollution
language:en
Short-container-title:Water Air Soil Pollut

Author:

Lima E. S.,Lima V. A.,Almeida C. A. P.,Justi K. C.

Publisher

Springer Science and Business Media LLC

Subject

Pollution,Water Science and Technology,Ecological Modeling,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s11270-017-3443-0/fulltext.html

Reference34 articles.

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2. Bettinelli, M., Beone, G., Spezia, S., & Baffi, C. (2000). Determination of heavy metals in soils and sediments by microwave-assisted digestion and inductively coupled plasma optical emission spectrometry analysis. Analytica Chimica Acta, 424(2), 289–296. https://doi.org/10.1016/S0003-2670(00)01123-5 .

3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 .

4. Chand, V., & Prasad, S. (2013). ICP-OES assessment of heavy metal contamination in tropical marine sediments: a comparative study of two digestion techniques. Microchemical Journal, 111, 53–61. https://doi.org/10.1016/j.microc.2012.11.007 .

5. Chen, M., & Ma, L. Q. (2001). Comparison of three aqua regia digestion methods for twenty Florida soils. Soil Science Society of America Journal, 65(2), 491. https://doi.org/10.2136/sssaj2001.652491x .

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