AI-Driven Solution Selection: Prediction of Water Quality Using Machine Learning

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Author(s): Tran Thi Hong Ngoc (An Giang University, Vietnam), Phan Truong Khanh (An Giang University, Vietnam)and Sabyasachi Pramanik (Haldia Institute of Technology, India)
Copyright: 2024
Pages: 15
Source title: Using Traditional Design Methods to Enhance AI-Driven Decision Making
Source Author(s)/Editor(s): Tien V. T. Nguyen (Industrial University of Ho Chi Minh City, Vietnam)and Nhut T. M. Vo (National Kaohsiung University of Science and Technology, Taiwan)
DOI: 10.4018/979-8-3693-0639-0.ch007

Keywords: Art and Design / Artificial Intelligence / Computer Science & IT / Information Science Reference

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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.

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AI-Driven Solution Selection: Prediction of Water Quality Using Machine Learning

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