Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors.

By N. M. Gazzaz, M. K. Yusoff, A. Aris, H. Juahir, M. Ramli. Marine Pollution Bulletin, 2012

374 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c88706bca11bde7c8ab6e632fdf66501d26a3471

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. Yeqi Liu
2. Rahim Barzegar
3. Daoliang Li
4. Ling Yang
5. Lihua Song
6. Yingyi Chen

In Water Science and Technology

1. Yeqi Liu
2. Rahim Barzegar
3. Daoliang Li
4. Ling Yang
5. Lihua Song
6. Yingyi Chen

Citations in the corpus

• A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Range scaling [132] and standardization [113] are two popular categories in normalization.”
• Efficient Water Quality Prediction Using Supervised Machine Learning (2019, methodology): “[4] used 23 input parameters in their methodology, and Rankovic et al.”
• A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran (2016, methodology): “…the suitable model inputs, specifying network type, pre-processing and partitioning of the available data; determining network architecture; defining model performance criteria; training (optimization of connection weights); and validating the model (Dawson and Wilby 2001; Gazzaz et al. 2012).”