Evaluation of data driven models for river suspended sediment concentration modeling

By Mohammad Zounemat‐Kermani, Ozgur Kisi, Jan Adamowski, Abdollah Ramezani-Charmahineh. 2016

121 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/14ad2e3c31a2e38a51a576563ffc813e32e4a8e2

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. Amir Mosavi
2. Jan Adamowski
3. Kwok-Wing Chau
4. Yeqi Liu
5. S. M. Bateni
6. Daoliang Li
7. Saeed Samadianfard
8. Ling Yang

In Water Science and Technology

1. Amir Mosavi
2. Jan Adamowski
3. Kwok-Wing Chau
4. Yeqi Liu
5. Daoliang Li
6. Ling Yang
7. Lihua Song
8. Yingyi Chen

Citations in the corpus

• Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems (2019, methodology): “To achieve prediction accuracy, the choice of suitable kernels and appropriate parameters is important (Zounemat-Kermani et al. 2016).”
• A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, methodology): “Feedforward [82] Hydrometric stations (USA) SS;; NA Q Category 0 and Category 3 No details (8 years) daily Train and test:80%, validate:20% ANN(MLP), SVR, MLR 1”
• Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021, background): “It is important to choose the right parameters to achieve a reliable predictive accuracy (Zounemat-Kermani et al., 2016).”