Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

By Fi-John Chang, Ya-Ting Chang. 2006

578 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0ec66b0edf5a0947e299236396a2a56a84b834ec

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. Annamária R. Várkonyi-Kóczy
3. Pınar Öztürk
4. Kimon P. Valavanis
5. Timon Rabczuk
6. F. Ferdinand
7. Eftychios E. Protopapadakis
8. George S. Atsalakis

In Environmental Engineering

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Kimon P. Valavanis
5. Timon Rabczuk
6. Eftychios E. Protopapadakis
7. George S. Atsalakis
8. Peter M. Atkinson

In Global and Planetary Change

1. Amir Mosavi
2. Pınar Öztürk
3. Kwok-Wing Chau
4. Kimon P. Valavanis
5. Eftychios E. Protopapadakis
6. George S. Atsalakis
7. Peter M. Atkinson
8. Pedram Ghamisi

Citations in the corpus

• High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach (2019, background): “The latter two are often used for representing fuzzy sets because of the smooth distribution curves [59].”
• Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, methodology): “The proposed model (Chang and Chang 2006) is used to forecast short term share trends, using neuro-fuzzy rules.”
• Flood Prediction Using Machine Learning Models: Literature Review (2018, background): “…Water 2018 , 10 , 1536 19 of 40 where x i and y i are the observed and predicted values and the i -th residue; x and y are their means, respectively. where X obs defines observed variables and X model prediction values for year i , where generally R 2 > 0.8 is considered as an acceptable prediction.”
• Comparison of fuzzy inference systems for streamflow prediction (2009, background): “…processes (Abebe et al., 2000; Hundecha et al., 2001; Jacquin & Shamseldin, 2006); river flow routing (See & Openshaw, 2000; Chang & Chang, 2001); groundwater modelling (Hong et al., 2002); water-level prediction in reservoirs (Chang & Chang, 2006); and time series modelling (Nayak et al., 2004).”
• COVID-19 Outbreak Prediction with Machine Learning (2020, background): “…Achieving a flexible partition for nonlinear inversion is non-trivial. build a neural network whose outputs are a degree of the input that The idea of this model is to build a neural network whose outputs are a degree of the input that belongs to each class [92–94]. belongs to each class [92–94].”