POLLUTION LEVEL ANALYZER USING MACHINE LEARNING WITH GEOSPATIAL DATA

Air pollution has become one of the most critical environmental issues affecting human health and ecological balance, particularly in rapidly growing urban areas. Traditional air quality monitoring systems rely on a limited number of monitoring stations, which makes it difficult to obtain detailed spatial information about pollution levels across large geographic regions. To address this limitation, this study proposes a Pollution Level Analyzer that integrates machine learning techniques with geospatial data analysis to predict and visualize pollution levels across different locations. The proposed system collects environmental data such as temperature, humidity, wind speed, and pollutant concentrations including particulate matter and harmful gases. Machine learning algorithms are applied to analyze historical environmental data and identify patterns that influence pollution levels. These models are trained to predict pollution levels for specific locations based on environmental conditions. In addition, geospatial data processing is used to associate pollution data with geographic coordinates, enabling visualization of pollution distribution on digital maps. The integration of machine learning with geospatial mapping allows users to identify pollution hotspots, understand spatial pollution trends, and make informed decisions regarding environmental management. The proposed system provides an efficient and scalable approach for monitoring air quality and predicting pollution levels. This approach can support environmental agencies, urban planners, and policymakers in implementing effective pollution control strategies and improving public health. Furthermore, the proposed system emphasizes the importance of data-driven environmental monitoring by combining advanced analytical techniques with spatial intelligence. By utilizing machine learning models, the system can automatically analyze large volumes of environmental data and detect hidden relationships between meteorological conditions and pollutant concentrations. This capability improves the accuracy of pollution prediction and helps in understanding how environmental factors contribute to variations in air quality over time and space. The use of predictive analytics allows authorities to anticipate potential pollution increases and taken preventive measures before the air quality reaches hazardous levels. Another significant advantage of the system is its ability to support location-based environmental analysis. Through geospatial integration, pollution data can be examined in relation to geographic features, urban density, traffic patterns, and industrial areas. This spatial understanding helps researchers and policymakers identify areas that are more vulnerable to pollution and prioritize actions for pollution mitigation. The visualization component further enhances the usability of the system by presenting complex environmental data in an intuitive and easy-to-understand format. The system also promotes accessibility and transparency in environmental information by enabling users to interact with pollution data through visual maps and analytical results. Such tools can increase public awareness about air quality conditions and encourage communities to adopt environmentally responsible practices. By providing reliable predictions and spatial insights, the Pollution Level Analyzer contributes to the development of smarter and more sustainable cities. Overall, the proposed approach demonstrates how the integration of machine learning, environmental data analysis, and geospatial technologies can significantly enhance air quality monitoring systems. This framework not only improves the efficiency of pollution prediction but also supports longterm environmental planning and policy development aimed at protecting human health and maintaining ecological balance.