Machine learning models for water safety enhancement
Abstract Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially leading to cancer and genetic mutations. A st...
Saved in:
Main Authors: | Fatemeh Ranjbar, Hossein Sadeghi, Reza Pourimani, Soraya Khanmohammadi |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-88431-4 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Safety of Drinking Water from Primary Water Sources and Implications for the General Public in Uganda
by: Keneth Iceland, Kasozi, et al.
Published: (2019) -
Evaluating the occurrence of trihalomethanes in drinking water and their implications for human health risk
by: Victor Constantin Cojocaru, et al.
Published: (2024-12-01) -
Comprehensive assessment of water and sediment quality in Lake Nasser, Egypt, using various potential risk indices
by: Abdel-Satar Amaal M., et al.
Published: (2024-04-01) -
Water Risk and Mining Firms’ Stock Return
by: Maryam Davallou, et al.
Published: (2024-06-01) -
Laser Induced Spectroscopy (LIBS) Technology and Environmental Risk Index (RI) to Detect Microplastics in Drinking Water in Baghdad, Iraq
by: Estabraq Mohammed Ati, Shahla Hussien Hano, Rana Fadhil abbas, Reyam Naji Ajmi and Abdalkader Saeed Latif
Published: (2024-12-01)