Hydrogeochemical characterization of the groundwater of Lahore region using supervised machine learning technique

Ismail, Sadia; Ahmed, Muhammad Farooq

doi:10.1007/s10661-022-10648-x

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hydrogeochemical characterization of the groundwater of Lahore region using supervised machine learning technique

Cited by 5 publications

References 89 publications

The chemical state of the watershed in Ras Elma region (South of Taza, Morocco) one of the parameters responsible for the decline in the formation of the current travertine formations

The chemical state of the watershed in Ras Elma region (South of Taza, Morocco) one of the parameters responsible for the decline in the formation of the current travertine formations

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling

Geochemistry of groundwater: Major and trace elements

Contact Info

Product

Resources

About