Hybrid CEEMDAN–deep learning models for COD prediction in wastewater treatment plants

Environmental Science Water Research & Technology 2026 Open access

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Authors: Saeed Samadianfard, Egemen Aras, Damla Yilmaz Çelik, Mohammad Taghi Sattari, Orhan Gündüz
DOI: 10.1039/d6ew00014b
Cited by: 1
References: 30

Abstract

A hybrid CEEMDAN–GRU framework optimized with an MOOTLBO enables accurate real-time prediction of chemical oxygen demand (COD) in wastewater treatment plants using routinely monitored water-quality parameters.

Fields of Study & Subjects

Wastewater Treatment and Nitrogen Removal Water Quality Monitoring and Analysis Hydrological Forecasting Using AI

Publication details

Journal / source
Environmental Science Water Research & Technology
Publisher
Royal Society of Chemistry (RSC)
Volume
12
Issue
6
Pages
1872–1894
Publication date
1 January 2026
Language
EN