METHOD FOR PREDICTING THE AIR QUALITY INDEX (AQI) IN A SPECIFIED GEOGRAPHICAL AREA

Abstract

ABSTRACT A method (100) for predicting the Air Quality Index (AQI) in a specified geographical area. Further, the method comprising. Further, the method (100) comprising the steps of collecting historical air quality and meteorological data from multiple sources, including pollutant concentrations and weather conditions. Further, the method (100) comprising the steps of pre-processing the collected data to clean it by removing missing values and outliers, and normalize the data for consistency. Further, the method (100) comprising the steps of selecting relevant features that significantly influence AQI using statistical analysis or machine learning techniques. Further, the method (100) comprising the steps of training multiple machine learning models on the prepared dataset. Further, the method (100) comprising the steps of evaluating model performance using a testing dataset to identify the most accurate model, and use it to

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of air quality monitoring and prediction, and in particular relates to a method for predicting the Air Quality Index (AQI) using machine learning techniques that leverage historical air quality and meteorological data to provide accurate and timely assessments of environmental conditions.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The increasing concern over air pollution and its adverse effects on human health and the environmen , Claims:1. A method (100) for predicting the Air Quality Index (AQI) in a specified geographical area, the method comprising the steps of:
collecting historical air quality and meteorological data from multiple sources, including pollutant concentrations and weather conditions;
pre-processing the collected data to clean it by removing missing values and outliers, and normalize the data for consistency;
selecting relevant features that significantly influence AQI using statistical analysis or machine learning techniques;
training multiple machine learning models on the prepared dataset, including regression algorithms such as linear regression, support vector regression, decision tree regression, and random forest regression; and
evaluating model performance using a testing dataset to identify the most accurate model, and use it to predict real-time AQI values, providing results through a user interface.

2. The method (100) as claimed in claim 1, wherein the selected machine learning models include at least one of li

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