A neuro-fuzzy computing technique for modeling hydrological time series

By Purna C. Nayak, K. Sudheer, D. Rangan, K. Ramasastri. 2004

676 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/38bf65cfb44796dd9cabb479836f2f87aaf706c8

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. Mohammad Zounemat‐Kermani
3. Wen Wang
4. F. Mekanik
5. Biswajeet Pradhan
6. Lin Qiu
7. Trung-Quôc Le
8. Satish T. S. Bukkapatnam

In Electrical and Electronic Engineering

1. Kwok-Wing Chau
2. Mohammad Zounemat‐Kermani
3. Meysam Alizamir
4. Wen Wang
5. F. Mekanik
6. Lin Qiu
7. Trung-Quôc Le
8. Satish T. S. Bukkapatnam

In Environmental Engineering

1. Kwok-Wing Chau
2. Ozgur Kisi
3. Mohammad Zounemat‐Kermani
4. Meysam Alizamir
5. Biswajeet Pradhan
6. Abdüsselam Altunkaynak
7. Hadi Sanikhani
8. Lin Qiu

Citations in the corpus

• Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS (2012, background): “Since values of the membership functions of the ANFIS vary between 0 and 1, the input data needed to be scaled in the range of 0–1 (Nayak et al., 2004).”
• Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, background): “A considerable privilege of the soft computing techniques in system modeling is that there is no need for a well-defined physical relationship for transformation of an input to an output (Nayak et al. 2004).”
• Comparison of fuzzy inference systems for streamflow prediction (2009, background): “, 2002); water-level prediction in reservoirs (Chang & Chang, 2006); and time series modelling (Nayak et al., 2004).”
• A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series (2009, methodology): “Other researchers reported good results in applying ANFIS in hydrological prediction (Cheng et al., 2005; Keskin et al., 2006; Nayak et al., 2004).”
• Singular spectrum analysis and forecasting of hydrological time series (2006, methodology): “, have also been used in forecasting hydrologic variables (e.g. French et al., 1992; Hsu et al., 1995; Zealand et al., 1999; Sivakumar et al., 2002; Nayak et al., 2004).”
• Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals (2016, methodology): “This conclusion is supported by Nayak et al. (2004).”