Design of Rapid Warning System for Detection of Pollution and Anomalies in High Dimensional Process by Using Adaptive Network Fuzzy Inference System

Khayat, Monireh; Noorossana, Rassoul; Soleimani, Paria; Raissi, Sadig

doi:10.48306/juem.2026.583807.1157

Design of Rapid Warning System for Detection of Pollution and Anomalies in High Dimensional Process by Using Adaptive Network Fuzzy Inference System

Authors

Monireh khayat ¹

Rassoul Noorossana ²

Paria Soleimani ³

Sadig Raissi ⁴

¹ Islamic Azad University South Branch .Industrial Engineering Faculty

² Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

³ Isalamic Azad University,South Branch Of Tehran

⁴ Islamic Azad University ,South Branch Faculty

https://doi.org/10.48306/juem.2026.583807.1157

Abstract

This study inspects the impact of water pollution in the nonlinear uncertainty and high dimensional process to detect early abnormalities or significant changes in the water pollution indicators. A hybrid algorithm based on wavelet techniques and adaptive network fuzzy inference system learning by using high-frequency surrogate relations is introduced. Important qualitative indicators such as phosphate, and nitrate in the water bodies have uncertainties with variations such as dependence, and effectiveness of physical and chemical factors. A case study based on the monitoring program applied to the Potomac River Basin in Virginia, USA, is conducted. In the first step, the high-frequency time series of the main TP index is obtained through the surrogate model and compared with GARCH techniques. By using the wavelet transform, the noise components of the time series are removed and pre-processed. In the next step, it is created by using the ANFIS to identify the main characteristics of water quality. In the last step, the contamination threshold is calculated based on the estimated base pattern for analyzing statistical patterns. The results show that the proposed algorithm has high stability and accuracy because using the surrogate technique has extracted a more accurate model of the behavior of the required water variables. It can be used to manage surface runoff in watersheds to preserve the environment and improve water quality. Based on the results, using ANFIS models in water pollution anomaly results in accurate simulations.
Keywords: water pollution, anomaly, high dimensional process, adaptive network fuzzy inference system, wavelet

Keywords

water pollution

anomaly

high dimensional process

adaptive network fuzzy inference system

wavelet

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