Artificial neural network modeling of the river water quality—A case study

By Kunwar P. Singh, Ankita Basant, A. Malik, Gunja Jain. 2009

656 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/184ee75ed2f7c0cde7338be47a9f2f3a446079e8

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. Jan Adamowski
2. A. H. Baghanam
3. Vahid Nourani
4. Ozgur Kisi
5. Yeqi Liu
6. Daoliang Li
7. Ling Yang
8. Lihua Song

In Water Science and Technology

1. Jan Adamowski
2. Vahid Nourani
3. A. H. Baghanam
4. Ozgur Kisi
5. Yeqi Liu
6. Daoliang Li
7. Ling Yang
8. Lihua Song

Citations in the corpus

• A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran (2016, background): “Determining the number of hidden nodes is usually a trial and error task in ANN modeling (Palani et al. 2008; Singh et al. 2009; Aksoy and Dahamsheh 2009).”
• Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, background): “…and Datta (2009) He et al. (2008) Fallah-Mehdipour et al. (2013) Bhattacharjya and Datta (2009) Yoon et al. (2011) Water quality modeling Singh et al. (2009) Pai et al. (2009) Eslamian and Lavaei (2009) Dhar and Datta (2009) Singh et al. (2011) sub-signals at different temporal scales…”
• A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Feedforward [95] Gomti river (India) DO, BOD; pH, TA, TH, TS, COD, NH3-N, NO3, P RF NA Category 2 500 samples (10 years) monthly Train:60%, validate: 20%, test: 20% ANN NA”