Review of the application of fuzzy inference systems in river flow forecasting

Jacquin, A. P.; Shamseldin, Asaad Y.

doi:10.2166/hydro.2009.038

Cited by 32 publications

(12 citation statements)

References 59 publications

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“…Fuzzy models have already been applied to water quality problems (Liou et al 2003). Firat & Güngör (2008) and Jacquin & Shamseldin (2009) investigated the applicability and capability of fuzzy inference systems in river flow forecasting. Soyupak <& Chen (2004) developed a fuzzy logic model to estimate pseudo steady-state chlorophyll-a concentrations in a very large and deep Keban Dam Reservoir.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Ranković

Radulović

Radojević

et al. 2011

Journal of Hydroinformatics

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BSTRACTPredicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

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Section: Introductionmentioning

confidence: 99%

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Ranković

Radulović

Radojević

et al. 2011

Journal of Hydroinformatics

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“…Some researchers have successfully applied fuzzy inference systems in river flow forecasting (Nayak 2009). Jacquin & Shamseldin (2009) reviewed the existing studies dealing with the use of fuzzy inference systems in river flow forecasting. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural network models.…”

Section: Introductionmentioning

confidence: 99%

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Tiwari

Chatterjee

2010

Journal of Hydroinformatics

130

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A new hybrid model, the wavelet-bootstrap-ANN (WBANN), for daily discharge forecasting is proposed in this study. The study explores the potential of wavelet and bootstrapping techniques to develop an accurate and reliable ANN model. The performance of the WBANN model is also compared with three more models: traditional ANN, wavelet-based ANN (WANN) and bootstrapbased ANN (BANN). Input vectors are decomposed into discrete wavelet components (DWCs) using discrete wavelet transformation (DWT) and then appropriate DWCs sub-series are used as inputs to the ANN model to develop the WANN model. The BANN model is an ensemble of several ANNs built using bootstrap resamples of raw datasets, whereas the WBANN model is an ensemble of several ANNs built using bootstrap resamples of DWCs instead of raw datasets. The results showed that the hybrid models WBANN and WANN produced significantly better results than the traditional ANN and BANN, whereas the BANN model is found to be more reliable and consistent. The WBANN and WANN models simulated the peak discharges better than the ANN and BANN models, whereas the overall performance of WBANN, which uses the capabilities of both bootstrap and wavelet techniques, is found to be more accurate and reliable than the remaining three models.

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“…These systems are widely applied in di®erent¯elds such as data classi¯cation, automatic control, expert systems, decision-making, robotics, time series analysis, pattern classi¯cation, process planning, and system identi¯cation. 1,4,24 A Fuzzy Inference System consists of three principal components: (1) a rule base, comprising of the selected fuzzy rules; (2) a database, maintaining the membership functions of the fuzzy rules; and (3) a reasoning mechanism, performing a fuzzy inference procedure upon the rules to derive a reasonable output or conclusion. 25 Each fuzzy rule consists of antecedent and consequent parts.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Application of the Shuffled Frog Leaping Algorithm (SFLA) in Constructing Fuzzy Classification Systems

Mirhosseini

Haghighi

2019

Int. J. Comp. Intel. Appl.

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A Fuzzy Inference System (FIS) is a way of mapping an input space to an output space using the fuzzy logic. FISs are widely used to solve classification problems. The Shuffled Frog Leaping Algorithm (SFLA) is a metaheuristic inspired by the natural evolution of frogs in searching for the largest source of food. By using local and global searches simultaneously, SFLA is effective in solving various optimization problems. This paper first proposes a new method to create zero-order Sugeno Fuzzy Inference Systems using SFLA. Then, the paper introduces an approach to use resulting SFLA-based Fuzzy Inference Systems to build test oracles. Test oracle is a mechanism for determining whether a test on a software program has passed or failed. The experimental results show that SFLA creates fuzzy systems more efficiently than three other evolutionary algorithms, including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Moreover, with respect to the accuracy and convergence speed criteria, SFLA and PSO outperform other evolutionary algorithms, while their performances are comparable to each other. At last, the experimental results indicate that SFLA-based FISs can be used to create test oracles with acceptable accuracy.

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Review of the application of fuzzy inference systems in river flow forecasting

Cited by 32 publications

References 59 publications

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Application of the Shuffled Frog Leaping Algorithm (SFLA) in Constructing Fuzzy Classification Systems

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