The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
AI-Driven Solution Selection: Prediction of Water Quality Using Machine Learning
Abstract
With the fast growth of aquatic data, machine learning is essential for data analysis, categorization, and prediction. Data-driven models using machine learning may effectively handle complicated nonlinear problems in water research, unlike conventional approaches. Machine learning models and findings have been used to build, monitor, simulate, evaluate, and optimize water treatment and management systems in water environment research. Machine learning may also enhance water quality, pollution control, and watershed ecosystem security. This chapter discusses how ML approaches were used to assess water quality in surface, ground, drinking, sewage, and ocean. The authors also suggest potential machine learning applications in aquatic situations.
Related Content
|
Mohammed Adi Al Battashi, Mohamad A. M. Adnan, Asyraf Isyraqi Bin Jamil, Majid Adi Al-Battashi.
© 2026.
30 pages.
|
|
Potchong M. Jackaria, Al-adzran G. Sali, Hana An L. Alvarado, Rashidin H. Moh. Jiripa, Al-sabrie Y. Sahijuan.
© 2026.
26 pages.
|
|
Elizabeth Gross.
© 2026.
30 pages.
|
|
Siti Nazleen Abdul Rabu, Xie Fengli, Ng Man Yi.
© 2026.
44 pages.
|
|
Mohammed Abdul Wajeed.
© 2026.
30 pages.
|
|
Aldammien A. Sukarno, Al-adzkhan N. Abdulbarie, Wati Sheena M. Bulkia, Potchong M. Jackaria.
© 2026.
24 pages.
|
|
Abdulla Sultan Binhareb Almheiri, Humaid Albastaki, Hanadi Alrashdan.
© 2026.
26 pages.
|
|
|