AIDriven Environmental Monitoring and Forecasting System

Project Code :TCMAPY1546

Objective

This project develops an AI-driven system for environmental monitoring and forecasting using deep learning and machine learning techniques. It predicts Air Quality Index (AQI) using models like Linear Regression, ARIMA, and CNN+LSTM, and classifies satellite images into environmental categories using MobileNet and CNN+LSTM.

Abstract

The project aims to develop an AI-driven environmental monitoring and forecasting system. The system has two main modules: a forecasting module and an image classification module. The forecasting module predicts the Air Quality Index (AQI) using various environmental data like PM2.5, NO2, CO, and others. By analyzing these inputs, the system helps in understanding the air quality and predicting possible health risks. The image classification module uses deep learning techniques to classify satellite images into categories such as 'agricultural', 'airplane', 'baseballdiamond', 'beach', 'buildings', 'chaparral', 'denseresidential', 'forest', 'freeway', 'golfcourse', 'harbor', 'intersection', 'mediumresidential', 'mobilehomepark', 'overpass', 'parkinglot', 'river', 'runway', 'sparseresidential', 'storagetanks', 'tenniscourt', 'unknow'. The system integrates machine learning algorithms, including CNN+LSTM and MobileNet, to process and classify data effectively. This project will help improve environmental awareness and provide timely information to help mitigate the impact of air pollution and environmental changes. Keywords: Air Quality, AQI Prediction, Environmental Monitoring, Satellite Image Classification, Deep Learning, CNN+LSTM, MobileNet, PM2.5, Air Pollution, Machine Learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.