A COMPARATIVE STUDY OF ANN AND ANFIS TO PREDICT THE AIR QUALITY INDEX
Keywords:
Adaptive Neural Fuzzy Inference System(ANFIS), Air Quality Index(AQI), Air Quality Prediction, Artificial Neural Networks(ANNs)Abstract
The study aims to compare the performance of Artificial Neural Networks (ANNs) and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in predicting the Air Quality Index (AQI). The Air Quality Index is an important tool for assessing the level of pollution in the air and its impact on human health. Artificial intelligence techniques have been used to analyze complex environmental data that includes multiple pollutants such as ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, and fine particulate matter. The ANN and ANFIS models were built and tested using a set of real environmental data, The dataset contains over 10,000 air quality readings from various cities in India, where the accuracy of the models was evaluated by comparing the predicted results to the actual AQI values. The results of the study showed that the ANFIS system is distinguished by its ability to deal with ambiguous and complex data more effectively than ANN, while ANN is more fitting when dealing with structured data. The study recommended building hybrid models that combine the advantages of ANN and ANFIS and applying these developed hybrid predictive models in actual environmental monitoring systems to contribute to improving the accuracy of predictions and providing more reliable information to help in making decisions to protect the environment and public health and reduce the negative effects of air pollution.
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