Year: 2025
Type: article
Abstract: This research focuses on measuring river pollution due to industrial activities using machine learning (ML) models. The goal is to create and assess ML algorithms which, given specific environmental and industrial indicators, could reliably forecast the level of pollutants. The approach includes gathering information, feature selection, and model training with techniques including Artificial Neural Networks (ANN) and Random Forests (RF). Results clearly reveal that ML models attain a high level of accuracy which allows the sophisticated control of pollution and the development of early alert systems for pollution. It is shown that ML can significantly assist in the management of the environment and water resources, which is vital for industries and decision makers who strive for the reduction of environmental harm.
DOI: 10.64252/0f09x630
Authors: Mariyam Ahmed, Dr. Gaurav Tamrakar, Sayanti Benerjee
Institution: Kalinga University
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412022317