Wavelet and neuro-fuzzy conjunction model for streamflow forecasting

2011 ◽  
Vol 42 (6) ◽  
pp. 447-456 ◽  
Author(s):  
Özgür Kişi ◽  
Turgay Partal
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Model ◽  
Discrete Wavelet ◽  
Streamflow Forecasting ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Comparison Results ◽  
Monthly Streamflow ◽  
Models Comparison ◽  
Streamflow Data

In this study the wavelet-neuro-fuzzy model, which combines the wavelet transform and the neuro-fuzzy technique, has been employed to forecast monthly streamflows. The observed monthly streamflow data are decomposed into some sub-series (components) by discrete wavelet transform and then appropriate sub-series are used as inputs to the neuro-fuzzy models for forecasting monthly streamflows. The data from two stations, Durucasu and Tanir, in Turkey are used as case studies. The wavelet-neuro-fuzzy forecasts are compared with those of the single neuro-fuzzy models. Comparison results indicate that the wavelet-neuro-fuzzy model is superior to the classical neuro-fuzzy method especially for the peak values. For the Durucasu and Tanir stations, it was found that the wavelet-neuro-fuzzy models are superior in forecasting monthly streamflows than the optimal neuro-fuzzy models.

Wavelet and neuro-fuzzy conjunction model for predicting water table depth fluctuations

2012 ◽  
Vol 43 (3) ◽  
pp. 286-300 ◽  
Author(s):  
Ozgur Kisi ◽  
Jalal Shiri
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Model ◽  
Groundwater Depth ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Depth Data ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Comparison Of The Results ◽  
Better Than

The ability of a wavelet and neuro-fuzzy conjunction technique for groundwater depth forecasting was investigated in this study. The wavelet-neuro-fuzzy model was improved by combining two methods, the discrete wavelet transform and the neuro-fuzzy model. The conjunction model was applied to different input combinations of daily groundwater depth data of Bondville and Perry wells. Root mean square error (RMSE) and correlation coefficient (R) statistics were used for evaluating the accuracy of wavelet-neuro-fuzzy models. The accuracy of the conjunction models was compared with those of the single neuro-fuzzy models in one-, two- and three-day-ahead groundwater depth forecasting. Comparison of the results revealed that the wavelet-neuro-fuzzy models perform better than the neuro-fuzzy models especially for the two- and three-day-ahead forecasting cases.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

scholarly journals A Bayesian approach to probabilistic streamflow forecasts

2012 ◽  
Vol 15 (2) ◽  
pp. 381-391 ◽  
Author(s):  
Hui Wang ◽  
Brian Reich ◽  
Yeo Howe Lim
Keyword(s):  
Climate Models ◽  
Principal Component ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Streamflow Forecasting ◽  
Promising Alternative ◽  
Probabilistic Information ◽  
Monthly Streamflow ◽  
Streamflow Forecasts ◽  
Streamflow Data

One-month-ahead streamflow forecasting is important for water utilities to manage water resources such as irrigation water usage and hydropower generation. While deterministic streamflow forecasts have been utilized extensively in research and practice, ensemble streamflow forecasts and probabilistic information are gaining more attention. This study aims to examine a multivariate linear Bayesian regression approach to provide probabilistic streamflow forecasts by incorporating gridded precipitation forecasts from climate models and lagged monthly streamflow data. Principal component analysis is applied to reduce the size of the regression model. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior distribution of model parameters. The proposed approach is tested on gauge data acquired during 1961–2000 in North Carolina. Results reveal that the proposed method is a promising alternative forecasting technique and that it performs well for probabilistic streamflow forecasts.

Streamflow Prediction by Applying Generalized Regression Network with Time Series Decomposition Method

2016 ◽  
Vol 4 (3) ◽  
pp. 611 ◽  
Author(s):  
Muhammad Tayyab ◽  
Jianzhong Zhou ◽  
Rana Adnan ◽  
Changqing Meng ◽  
Aqeela Zahra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discrete Wavelet ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Water Resources Systems ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Generalized Regression ◽  
Mean Square Errors

Precise and correct estimation of streamflow is important for the operative progression in water resources systems. The artificial intelligence approaches; such as artificial neural networks (ANN) have been applied for efficiently tackling the hydrological matters like streamflow forecasting in this study at upper Yangtze River. The objective is to investigate the certainty of monthly streamflow by applying artificial neural networks including Generalized Regression Network (GRNN). To overcome the non-linearity problem of streamflow, artificial neural networks integrated with discrete wavelet transform (DWT). Data has been analyzed by comparing the simulation outputs of the models with the correlation coefficient (R) root mean square errors (RMSE). It is found that the decomposition technique DWT has ability to improve the forecasting results as compare to single applied artificial neural networks. Moreover, all applied models are separately applies on the peak values as well which also have showed that intergrated model has more ability to catch the peak values

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

scholarly journals Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System

2021 ◽  
Vol 12 (3) ◽  
pp. 4892-4903
Author(s):  
K.RamaMohana Reddy Et. al.
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Convergence Time ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Methods ◽  
Discrete Wavelet Packet Transform ◽  
Neuro Fuzzy

With the development of the technologies, the demand for good quality of electric power is increasing day by day. In Distributed Generation Systems (DGs), the quality of power can cause serious problems such as sensitive equipment's malfunction, the temperature riseof machines. Therefore, detection of power quality events in the power system is more important to take further actions. The existing power quality events classification methods have high computational time with low accuracy. In order to overcome this problem, this paper presents Discrete Packet Wavelet Transform-Kalman filter based Adaptive Neuro-Fuzzy approach for identification and classification of PQ events. The proposed method classifies the events with better classification accuracy, less convergence time and low in error prediction. The results show that the proposed method has better performance compared with the existing classification methods. The proposed method is Implemented and tested using MATLAB and it provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks etc..

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