Design of an Alarm System for Isfahan Ozone Level based on Artificial Intelligence Predictor Models

TL;DR

This paper develops an AI-based alarm system for predicting ozone levels in Isfahan using sensor data and compares different AI models to identify the most effective one for real-world application.

Contribution

It introduces a novel ozone level alarm system utilizing AI models and compares BEL, ANFIS, and ANN to determine the best predictive approach.

Findings

AI models successfully predict next-day ozone levels.

The system can trigger alarms based on predicted ozone concentrations.

ANFIS outperforms other models in accuracy.

Abstract

The ozone level prediction is an important task of air quality agencies of modern cities. In this paper, we design an ozone level alarm system (OLP) for Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011. We propose a computer based system with three inputs and single output. The inputs include three sensors of solar ultraviolet (UV), total solar radiation (TSR) and total ozone (O3). And the output of the system is the predicted O3 of the next day and the alarm massages. A developed artificial intelligence (AI) algorithm is applied to determine the output, based on the inputs variables. For this issue, AI models, including supervised brain emotional learning (BEL), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), are compared in order to find the best model. The simulation of the proposed system shows that it can be used successfully in prediction of major cities ozone level.