Estimation of Daily Suspended Sediment Load by Using Wavelet Conjunction Models

By Jalal Shiri, Ozgur Kisi. 2012

54 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9fb803bc53d28e8e8dcbd8e457dcf75d5468db84

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. Jan Adamowski
2. Vahid Nourani
3. A. H. Baghanam
4. Ozgur Kisi
5. Kwok-Wing Chau
6. C. Wu
7. Yan Li
8. K. Sun

In Environmental Engineering

1. Jan Adamowski
2. A. H. Baghanam
3. Vahid Nourani
4. Ozgur Kisi
5. Kwok-Wing Chau
6. Masoud Karbasi
7. Ravinesh C. Deo
8. Mukesh Tiwari

In Water Science and Technology

1. Jan Adamowski
2. Vahid Nourani
3. A. H. Baghanam
4. Ozgur Kisi
5. Masoud Karbasi
6. Kwok-Wing Chau
7. Ravinesh C. Deo
8. John M. Quilty

Citations in the corpus

• Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “…loads, acceptably simulated the hysteresis phenomenon, and satisfactorily estimated the cumulative SSL. Employing GEP, NF, and ANN techniques to estimate SSL using daily river discharge and sediment load records, Shiri and Kisi (2012) showed that the GEP model outperformed the NF and ANN models.”