Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic

Toprak, Z. Fuat; Eriş, Ebru; Ağıralioğlu, Necati; Ciğizoğlu, H. Kerem; Yilmaz, Levent; Aksoy, Hafzullah; Coskun, H. Gonca; Andic, Gokhan; Algancı, Uğur

doi:10.1002/clen.200800152

Cited by 19 publications

(6 citation statements)

References 25 publications

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“…Continuous streamflow records in such cases can provide systematic prediction models using time series analysis. In recent literature, due to advances in computing systems, application of artificial intelligence (AI) techniques for cross-station or singlestation daily or monthly streamflow prediction has been investigated, and successful results have been reported (Ochoa-Rivera et al 2002;Kisi and Cigizoglu 2007;Kisi 2008;Demirel et al 2009;Toprak et al 2009;Besaw et al 2010;Can et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

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Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Ç oruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-meansquare error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

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Section: Introductionmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Mehr

Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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“…A Fuzzy Inference System (FIS) uses the process of mapping from a given set of input variables to an output based on a set of human understandable fuzzy rules [2]. FIS has been successfully applied in various applications, such as pattern recognition, data analysis and system control [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

Kajornrit

Wong

Fung

2012

2012 IEEE International Conference on Fuzzy Systems

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Abstract-In water management systems, accurate rainfall forecasting is indispensable for operation and management of reservoir, and flooding prevention because it can provide an extension of lead-time of the flow forecasting. In general, time series prediction has been widely applied to predict rainfall data. The conventional time series prediction models or artificial neural networks can be used to perform this task. However, such models are difficult to interpret by human analyst. From a hydrologist's point of view, the accuracy of the prediction and understanding the prediction model are equally important. This study proposes the use of a Modular Fuzzy Inference System (Mod FIS) to predict monthly rainfall data in the northeast region of Thailand. The experimental results show that the proposed model can be a good alternative method to provide both accurate results and human-understandable prediction mechanism.

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“…Even using the phase space reconstruction to identify input may not be a good solution to this problem. However, as the monthly time series is periodic in nature, adding time coefficient as a supplementary feature is a promising approach [20], [21], [22].…”

Section: A Input Identificationmentioning

confidence: 99%

An integrated intelligent technique for monthly rainfall time series prediction

Kajornrit

Wong

Fung

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper proposes a methodology to create an interpretable fuzzy model for monthly rainfall time series prediction. The proposed methodology incorporates the advantages of artificial neural network, fuzzy logic and genetic algorithm. In the first step, the differences between the time series data are calculated and they are used to define the interval between the membership functions of a Mamdani-type fuzzy inference system. Next, artificial neural network is used to develop the model from input-output data and the established model is then used to extract the fuzzy rules. The parameters of the created fuzzy model are then optimized by using genetic algorithm. The proposed model was applied to eight monthly rainfall time series data in the northeast region of Thailand. The experimental results showed that the proposed model provided satisfactory prediction accuracy when compared to other commonly-used prediction models. Due to the interpretability nature of the model, human analysts can gain insight knowledge of the data to be modeled.

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Modeling Monthly Mean Flow in a Poorly Gauged Basin by Fuzzy Logic

Cited by 19 publications

References 25 publications

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

An integrated intelligent technique for monthly rainfall time series prediction

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