Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods

By Ozgur Kisi, A. Azad, Hamed Kashi, A. Saeedian, S. A. Hashemi, S. Ghorbani. Water resources management, 2019

78 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3d15a606999dd1a92775bb3f872af8e1ae076dea

How researchers see this paper

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

In Artificial Intelligence

1. Rahim Barzegar
2. Jan Adamowski
3. Mohammad Taghi Aalami

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): “…Mynett 2003; Pereira et al. 2009; Huang et al. 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. 2018), multivariate adaptive regression spline (MARS) (Kisi and Parmar 2016;…”