Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network

Kumar, Pavitra and Lai, Sai Hin and Mohd, Nuruol Syuhadaa and Kamal, Md Rowshon and Ahmed, Ali Najah and Sherif, Mohsen and Sefelnasr, Ahmed and El-Shafie, Ahmed (2021) Enhancement of nitrogen prediction accuracy through a new hybrid model using ant colony optimization and an Elman neural network. Engineering Applications of Computational Fluid Mechanics, 15 (1). pp. 1843-1867. ISSN 1994-2060, DOI https://doi.org/10.1080/19942060.2021.1990134.

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Advanced human activities, including modern agricultural practices, are responsible for alteration of natural concentration of nitrogen compounds in rivers. Future prediction of nitrogen compound concentrations (especially nitrate-nitrogen and ammonia-nitrogen) are important for countries where household water is obtained from rivers after treatment. Increased concentrations of nitrogen compounds result in the suspension of household water supplies. Artificial Neural Networks (ANNs) have already been deployed for the prediction of nitrogen compounds in various countries. But standalone ANN have several limitations. However, the limitations of ANNs can be resolved using hybrid models. This study proposes a new ACO-ENN hybrid model developed by integrating Ant Colony Optimization (ACO) with an Elman Neural Network (ENN). The developed ACO-ENN hybrid model was used to improve the prediction results of nitrate-nitrogen and ammonia-nitrogen prediction models. The results of new hybrid models were compared with multilayer ANN models and standalone ENN models. There was a significant improvement in the mean square errors (MSE) (0.196 -> 0.049 -> 0.012, i.e. ANN -> ENN -> Hybrid), mean absolute errors (MAE) (0.271 -> 0.094 -> 0.069) and Nash-Sutcliffe efficiencies (NSE) (0.7255 -> 0.9321 -> 0.984). The hybrid model had outstanding performance compared with the ANN and ENN models. Hence, the prediction accuracy of nitrate-nitrogen and ammonia-nitrogen has been improved using new ACO-ENN hybrid model.

Item Type: Article
Funders: Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya[GPF082A-2018], Institut Pengurusan dan Pemantauan Penyelidikan, Universiti Malaya[GPF031A-2019]
Uncontrolled Keywords: Nitrate-nitrogen;Ammonia-nitrogen;Ant colony optimization; Elman neural network;New ACO-ENN hybrid model
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 21 Jul 2022 03:14
Last Modified: 21 Jul 2022 03:14
URI: http://eprints.um.edu.my/id/eprint/33960

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