A cumulative-risk assessment method based on an artificial neural network model for the water environment-Reference-Cited by-同舟云学术

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A cumulative-risk assessment method based on an artificial neural network model for the water environment

Published:2021-01-25 Issue:34 Volume:28 Page:46176-46185
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Shi En,Shang Yanchen,Li Yafeng,Zhang Miao

Funder

National Major Science and Technology Projects of China

Natural Science Foundation of Liaoning Province

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-021-12540-6.pdf

Reference32 articles.

1. Abu-Ali H, Nabok A, Smith TJ (2019) Electro-chemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach. Anal Bioanal Chem 411:1–10. https://doi.org/10.1007/s00216-019-01853-8

2. Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20(7):851–871. https://doi.org/10.1016/j.envsoft.2004.05.001

3. Antwi P, Li J, Boadi PO, Meng J, Shi E, Deng K, Bondinuba FK (2017) Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresour Technol 228:106–115. https://doi.org/10.1016/j.biortech.2016.12.045

4. Bhaya A, Kaszkurewicz E (2004) Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method. Neural Netw 17(1):65–71. https://doi.org/10.1016/s0893-6080(03)00170-9

5. Bosgra S, Voet HVD, Boon PE, Slob W (2009) An integrated probabilistic framework for cumulative risk assessment of common mechanism chemicals in food: an example with organophosphorus pesticides. Regul Toxicol Pharmacol 54(2):124–133. https://doi.org/10.1016/j.yrtph.2009.03.004

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