A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system

Xiong, Lihua; Shamseldin, Asaad Y.; O’Connor, Kieran M.

doi:10.1016/s0022-1694(01)00349-3

Cited by 233 publications

(135 citation statements)

References 35 publications

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“…Since then there have been several more studies which have dealt with multi-model combination of hydrological models (e.g. (Abrahart and See 2002, Ajami, et al 2006, Coulibaly, et al 2005, Hsu, et al 2009, See and Openshaw 2000, Shamseldin, et al 2007, Viney, et al 2009, Xiong, et al 2001). As the nature of the combination function is unknown and no theory exists to analytically derive the combination function from a hydrological or physical point of view, previous studies have used empirical data-driven modeling to derive the combination function and such use is very appropriate.…”

Section: Introductionmentioning

confidence: 99%

“…neural network and fuzzy-based) combination methods have been used to produce multi-model river flows (Coulibaly, et al 2005, See and Openshaw 2000, Shamseldin 1997, Xiong, et al 2001. The linear combination methods include the Simple Average Method (SAM) and the Weighted Average Method (WAM).…”

Section: Introductionmentioning

confidence: 99%

“…Shamseldin et al (1997) pioneered the use of an artificial neural network (ANN) for efficient multi-model combination of a group of rainfall-runoff models. In the previous studies ANN combination methods were found to be generally better than those of the linear weighting (WAM) and fuzzy-based methods (Xiong, et al 2001) (See and Abrahart 2001, Xiong, et al 2001. The Fuzzy-Based Combination Method (FBCM) uses a set of fuzzy IF-THEN rules to transform the synchronous individual model forecasts (operating as inputs) into a single combined output forecast.…”

Section: Introductionmentioning

confidence: 99%

“…The Fuzzy-Based Combination Method (FBCM) uses a set of fuzzy IF-THEN rules to transform the synchronous individual model forecasts (operating as inputs) into a single combined output forecast. See and Openshaw (2000) and Xiong et al (2001) introduced the use of fuzzy methods for the combination of river flow forecasts. Xiong et al(2001) concluded that a first Order Takagi-Sugeno type FBCM can yield results that are better than those of the best of the constituent models, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

2012

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This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments.The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are 'black-box' models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method.The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

2012

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“…Sugeno & Yasukawa (1993) have developed a fuzzy logic-based approach to qualitative modelling and have proposed the use of a fuzzy clustering method for the structure identification of models. Fuzzy logic approaches have been applied in the estimation of water resources for more than ten years Şen, 1998;Stuber et al, 2000;See & Openshaw, 2000;Hundecha et al, 2001;Xiong et al, 2001). Recently, neuro-fuzzy systems have been introduced in hydrology; taking the advantage of both FL and ANNs, i.e.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Keskin

Taylan

Terzi

2006

Hydrological Sciences Journal

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The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS.

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Data‐Driven Modeling and Computational Intelligence Methods in Hydrology

Solomatine

2005

Encyclopedia of Hydrological Sciences

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Along with the physically based (process) models based on mathematical descriptions of hydrological processes, the so‐called data‐driven models (DDM) are becoming popular. They are based on the use of methods of computational intelligence and machine learning and assume the presence of considerable amount of data describing the modeled phenomenon. The article covers various aspects of DDM, including the data preparation and a brief overview of the used techniques – neural networks, regression and model trees, instance‐based learning, and nonlinear dynamics. A number of references to the successful application of data‐driven methods are provided along with the links to the relevant web sites and software packages. An example where several data‐driven methods were used in a hydrologic forecasting problem is provided.

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A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system

Cited by 233 publications

References 35 publications

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series

Data‐Driven Modeling and Computational Intelligence Methods in Hydrology

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