The impact of economic and IoT technologies on air pollution: an AI-based simulation equation model using support vector machines

In recent years, rapid technological advancements in artificial intelligence (AI) and the Internet of Things (IoT) have brought significant transformations across various industries and sectors. These advancements have created unprecedented opportunities for leveraging data-driven insights and informed decision-making. Urban populations swiftly adopt information technology solutions to monitor and regulate noise levels and environmental pollutants effectively. This research aims to bridge the traditional gap between environmental science and economics by harnessing cutting-edge AI and IoT technologies. Support vector machine (SVM) is a robust machine learning algorithm known for its ability to decipher complex, non-linear relationships within large and intricate datasets. This study constructs a robust simultaneous equation model by integrating economic indicators, real-time IoT sensor data, and relevant covariates. Mitigate the health risks associated with air pollution and raise awareness of its detrimental impacts. Air pollution continues to pose challenges to intelligent and sustainable healthcare systems. The healthcare ecosystem, coupled with various machine learning techniques, seeks to mitigate pollution’s impact and enhance residents’ quality of life by offering intelligent services. The analysis of the reciprocal relationship and impact mechanisms between economic development and air pollution is substantiated by empirical data. Formulates an air pollution index using principal component analysis but also develops simultaneous equations that account for spatial effects, urban panel data, and interactions between economic development and air pollution. The experimental results highlight the efficacy of the proposed SVM, achieving an impressive accuracy rate of 84.5%. This performance outstrips alternative methods such as K-Nearest Neighbors, Random Forest, Decision Trees, Logistic Regression, and XGBoost algorithms, reaffirming SVM’s prowess in accurately modeling the intricate relationship between economic development and air pollution, particularly when utilizing IoT data.