NDLI: Daily runoff time-series prediction based on the adaptive neural fuzzy inference system

Content Provider	IEEE Xplore Digital Library
Author	Qiaofeng Tan Xu Wang Siyu Cai Xiaohui Lei
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Simulation & Regul. of Water Cycle in River Basin, Inst. of Water Resource & Hydraulic Res., Beijing, China (Xu Wang; Siyu Cai; Xiaohui Lei) \|\| Inst. of Water Resources & Hydropower Eng., Sichuan Univ., Chengdu, China (Qiaofeng Tan)
Abstract	Artificial neural network and fuzzy inference technology have been successfully used in various fields in the last decades. In order to combine the advantages of these approaches, the previous researchers came up with a new model named adaptive neural fuzzy inference system (ANFIS), which has been applied to signal processing and the related fields. Hydrological prediction is an important aspect of hydrological services for economy and society. The prediction result not only provides decision support for generation optimization, but also is of great significance to the economical operation of hydropower systems, navigation, flood control and so on. This paper presents the application of adaptive neural fuzzy inference system (ANFIS) on daily runoff time-series prediction at Tongzilin station. To evaluate the performances of the selected ANFIS, comparison was made with the ANN and autoregressive (AR) model. Previous inflows were chosen as input vectors of the three different models. Nash-Sutcliffe efficiency coefficient (NS coefficient), root mean square error (RMSE) and mean absolute relative error (MARE) were chosen to evaluate the performances of our models. The results show that ANFIS not only keep the potential of the ANN whose advantage is to deal with nonlinear problem, but it also eases the model building process and makes the result more stable. As a result, ANFIS can be a recommended daily runoff time-series prediction model.
Starting Page	506
Ending Page	512
File Size	347158
Page Count	7
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7381994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Adaptation models artificial neural network Correlation Computational modeling Artificial neural networks autoregressive model Predictive models daily runoff prediction Mathematical model adaptive neural fuzzy inference system
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Prediction of annual runoff using adaptive network based fuzzy inference system

The Case Study of Adaptive Network-Based Fuzzy Inference System Modeling for TAIEX Prediction

Adaptive Multidimensional Neuro-Fuzzy Inference System for Time Series Prediction

Soft computing models to predict daily temperature of Dhaka

An adaptive fuzzy wavelet neural network with gradient learning algorithm for nonlinear function approximation

Prediction of university enrollment using computational intelligence

An Adaptive Hybrid Algorithm for Time Series Prediction in Healthcare

Soft computing approach for predictive blood glucose management using a fuzzy neural network

Grey adaptive-network-based fuzzy inference system for fund volatility forecasting

Daily runoff time-series prediction based on the adaptive neural fuzzy inference system

Similar Documents

Prediction of annual runoff using adaptive network based fuzzy inference system

The Case Study of Adaptive Network-Based Fuzzy Inference System Modeling for TAIEX Prediction

Adaptive Multidimensional Neuro-Fuzzy Inference System for Time Series Prediction

Soft computing models to predict daily temperature of Dhaka

An adaptive fuzzy wavelet neural network with gradient learning algorithm for nonlinear function approximation

Prediction of university enrollment using computational intelligence

An Adaptive Hybrid Algorithm for Time Series Prediction in Healthcare

Soft computing approach for predictive blood glucose management using a fuzzy neural network

Grey adaptive-network-based fuzzy inference system for fund volatility forecasting

Daily runoff time-series prediction based on the adaptive neural fuzzy inference system