Water quality polluted by total suspended solids classified within an Artificial Neural Network approach

TL;DR

This paper presents a convolutional neural network model that accurately predicts water pollution levels caused by suspended solids, offering a faster and more reliable alternative to traditional methods for real-time water quality monitoring.

Contribution

The study introduces a transfer learning-based neural network framework specifically designed for classifying water pollution levels from suspended solids data, improving prediction accuracy and efficiency.

Findings

High predictive accuracy of the neural network model.

Outperforms conventional statistical methods in speed and reliability.

Potential for real-time water pollution monitoring and management.

Abstract

This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-intensive. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations, with the goal of accurately predicting low, medium and high pollution levels based on various input variables. Our model demonstrated high predictive accuracy, outperforming conventional statistical methods in terms of both speed and reliability. The results suggest that the artificial neural network framework can serve as an effective tool for real-time monitoring and management of water pollution, facilitating proactive decision-making and policy formulation. This approach not only enhances our understanding of pollution dynamics but also underscores the potential of machine learning techniques in environmental science.