Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach

By B. Aghel, A. Rezaei, M. Mohadesi. International Journal of Environmental Science and Technology, 2018

59 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/ad26ab14ec6eafd25037ee6981f513efb0701c6a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Rahim Barzegar
2. Jan Adamowski
3. Mohammad Taghi Aalami
4. Ravinesh C. Deo
5. Mukesh Tiwari
6. Muhammad Mohsin Waqas
7. Usa Wannasingha Humphries
8. John M. Quilty

In Water Science and Technology

1. Rahim Barzegar
2. Jan Adamowski
3. Mohammad Taghi Aalami
4. Muhammad Mohsin Waqas
5. Usa Wannasingha Humphries

Citations in the corpus

• Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “2018), neuro fuzzy methods (Noori et al. 2013; Barzegar et al. 2016a; Khadr 2017; Aghel et al. 2019; Kisi et al. 2019; Zhu et al. 2019), M5P model-Tree (Yi et al.”