Air Pollution Standard Index (APSI) Detection Application Based on the Flask Model

Abstract

Air pollution is a global environmental problem that threatens human health and ecosystems. The Air Pollution Standards Index (APSI) is an important metric for measuring air quality and informing the public about the pollution level in an area. In the digital era, web-based applications have become an effective tool for providing real-time APSI information to the public. This research introduces an Air Pollution Standard Index (APSI) detection application based on the Flask model using the SVM (Support Vector Machine) algorithm to predict APSI. This application collects air quality data from various sensors distributed throughout the region and uses SVM (Support Vector Machine) to process the data. APSI prediction results are then presented to users via an easy-to-use web interface. The main advantage of this application is its ability to provide real-time APSI information so that users can take appropriate action according to the level of air pollution in their area. This application can help the public and environmental authorities proactively deal with air pollution and protect human and environmental health. APSI Prediction Accuracy: Through SVM model training, this application can predict the Air Pollution Standard Index (APSI) with sufficient accuracy. While there is potential to improve accuracy through more data collection and model updates, initial results are promising