Work

Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models

Year: 2024

Type: article

Abstract: O production pathways could improve the model's accuracy for longer forecasting horizons. These findings advocate for hybridising deep learning models with biokinetic and mechanistic insights to enhance prediction accuracy and interpretability.

Source: Water Research

DOI: 10.1016/j.watres.2024.122754

Authors: Siddharth Seshan, Johann Poinapen, Marcel H. Zandvoort, Jules B. van Lier, Zoran Kapelan

Institutions: KWR Water Research Institute, Delft University of Technology

Language: English

Topic: Wastewater Treatment and Nitrogen Removal

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action

Open Access status: hybrid

Estimated APC paid: $4,520