Water, Air Quality Analysis and Prediction System

Aisha Sidiq, Prof Preethi Thomas

Abstract: The Water and Air Quality Analysis and Prediction System aims to address growing concerns regarding environmental sustainability and public health. Developed using Python and MySQL, the system leverages machine learning techniques, specifically the Random Forest algorithm, to analyze and predict water quality based on parameters such as pH, dissolved oxygen, turbidity, conductivity, and temperature. By classifying water quality as "safe" or "unsafe", the system provides actionable insights to support resource management and environmental protection. Additionally, the air quality analysis component focuses on identifying pollution levels using critical indicators, enabling proactive interventions. The software incorporates data preprocessing steps, such as handling missing values and feature normalization, to optimize model performance. Integrated with a user-friendly interface and a secure database, the system delivers accurate predictions, detailed reports, and feedback. This innovative approach ensures scalability, adaptability, and efficiency, making it a vital tool for promoting sustainable development and safeguarding vital resources.

Keywords: water quality prediction, air quality prediction, random forest algorithm, artificial neural network