Artificial Neural Networks in Hydrology. I: Preliminary Concepts

By N. Null. 2000

1,430 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/61f886cb763d35ae0146037f9210c3c38cdb85a5

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. Kwok-Wing Chau
2. Ozgur Kisi
3. K. Sudheer
4. Lin Qiu
5. Vahid Karimi
6. A. Gosain
7. Wenchao Wang
8. Jianyi Lin

In Environmental Engineering

1. Kwok-Wing Chau
2. Ozgur Kisi
3. Chun-tian Cheng
4. Lin Qiu
5. Wenchao Wang
6. K. Sudheer
7. Jianyi Lin
8. Vahid Karimi

In Water Science and Technology

1. Kwok-Wing Chau
2. Ozgur Kisi
3. Chun-tian Cheng
4. K. Sudheer
5. Lin Qiu
6. Jianyi Lin
7. Wenchao Wang
8. A. Gosain

Citations in the corpus

• Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction (2020, background): “ANNs are well known for their ability to model nonlinear systems, such as precipitation-runoff, stream flow, and time series analysis (ASCE 2000).”