Rainfall-runoff modelling using adaptive neuro-fuzzy systems

Gautam, Dilip; Holz, Klaus-Peter

doi:10.2166/hydro.2001.0002

Cited by 46 publications

(18 citation statements)

References 5 publications

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“…The neuro-fuzzy system, the so-called ANFIS approach, was proposed by Jang (1993). It has been applied successfully to various hydrological problems, such as streamflow predictions (Gautam & Holz, 2001) and reservoir operations . Figure 1 illustrates the structure of the basic fuzzy inference system, which consists of four functional blocks, such as the fuzzy rule base, a fuzzifier, a fuzzy inference engine and a defuzzifier.…”

Section: Methodsmentioning

confidence: 99%

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Bae¹,

Jeong²,

Kim

2007

Hydrological Sciences Journal

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The purpose of this study is to evaluate the applicability of monthly weather forecasting information to the improvement of monthly dam inflow forecasts. The ANFIS (Adaptive Neuro-Fuzzy Inference System) is used to predict the optimal dam inflow, since it has the advantage of tuning the fuzzy inference system with a learning algorithm. A subtractive clustering algorithm is adopted to enhance the performance of the ANFIS model, which has a disadvantage in that the number of control rules increases rapidly as the number of fuzzy variables increases. To incorporate weather forecasting information into the ANFIS model, this study proposes a method for converting qualitative information into quantitative data. The ANFIS model for monthly dam inflow forecasts was tested in cases with and without weather forecasting information. It can be seen that the model performances obtained with the use of both past observed data and future weather forecasting information are much better than those using past observed data only.Key words neuro-fuzzy system; weather forecasting information; subtractive clustering; dam inflow Prévisions mensuelles de l'afflux dans un barrage en utilisant des informations de prévision météorologique et une technique neuro-floueRésumé Le but de cette étude est d'évaluer l'applicabilité de prévisions météorologiques mensuelles pour améliorer des prévisions mensuelles d'afflux dans un barrage. On utilise la méthode neuro-floue ANFIS (système adaptatif d'inférence neuro-floue) pour prévoir l'afflux optimal dans le barrage, parce qu'elle a l'avantage de régler le système d'inférence floue avec un algorithme d'apprentissage. Un algorithme soustractif de classification est adopté pour rendre plus performant le modèle ANFIS, dont un désavantage est que le nombre de règles de contrôle augmente rapidement avec le nombre de variables floues. Pour intégrer les informations de prévision météorologique dans le modèle ANFIS, cette étude propose une méthode de conversion des informations qualitatives en données quantitatives. Le modèle ANFIS de prévision mensuelle de l'afflux dans le barrage a été testé avec et sans les informations de prévision météorologique. Il apparaît que les performances du modèle obtenues en combinant des données du passé et des prévisions météorologiques sont bien meilleures que celles qui sont obtenues avec les seules données du passé.Mots clefs système neuro-flou; informations de prévision météorologique; classification soustractive; afflux dans un barrage

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Section: Methodsmentioning

confidence: 99%

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Bae¹,

Jeong²,

Kim

2007

Hydrological Sciences Journal

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“…Block-box modelling to simulate the rainfall-runoff relationship has been investigated extensively (for 25 example, Shamseldin and O'connor, 1999;Gautam and Holz, 2001;Talei et al, 2010). These models are fast and the results are often comparable with physical models.…”

Section: Relationship Between Rainfall and Water-level At Gauging Sitesmentioning

confidence: 99%

Real-time forecasting of typhoon inundation extent in a partially-gauged area through the integration of ARX-based models and a geographic information system

Ouyang

Chen²

2016

Preprint

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Abstract. This study presents a methodology for forecasting the extent of inundation and depth of distribution during typhoons in real-time. The proposed approach involves the construction of ARX and ARMAX models capable of predicting water-levels at the locations of on-site gauging stations and 10 representative points located at the outlets of the sub-areas obtained by terrain analysis using a geographic information system. The models are constructed based on historical typhoon data and the results of numerical simulations related to inundation. A database comprising layers of inundation maps related to water-levels in each sub-area based on the assumption of flat-water and the digital elevation model (DEM) of the area were assembled prior to the typhoon. Water-levels during the typhoon are forecast using the 15 constructed models, whereupon inundation sub-maps associated with the forecasted water-levels are extracted from the database. The resulting inundation map is comparable to that obtained using Synthetic Aperture Radar. Processing can be conducted in real-time and requires very little computational resources. This provides valuable lead time in which to conduct efforts aimed at damage mitigation during a typhoon. 20

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“…The advantage of using a neuro-fuzzy approach to develop a data modeling is the possibility to obtain a model that results not only accurate but also interpretable (Plantamura et al 2003). Moreover, the neuro-fuzzy approaches have been also used by (Gautam et al 2003;Nazemi et al 2004) for rainfall-runoff modeling. They reported that this technique effectives to increase the rainfall-runoff models for both forecasting and simulation.…”

Section: However Climate Fluctuation Of the Tropical Pacificmentioning

confidence: 99%

Neuro-Fuzzy Approaches for Modeling the Wet Season Tropical Rainfall

Annas¹,

Kanai²,

Koyama³

2006

Agricultural Information Research

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Information on rainfall variations is a matter of great importance in agricultural countries. Climate and rainfall are non-linear natural phenomena whose measurement leads to complex data, primarily due to noise patterns and distribution heterogeneity. Therefore, it is difficult to develop an appropriate model in practice by using conventional modeling techniques. This study presents the use of a neuro-fuzzy system for modeling wet season tropical rainfall. The advantage of this technique was the possibility of a modified environment of input parameters for improving the data representation. Two approaches used in the neuro-fuzzy models were classification and prediction. The neuro-fuzzy classification model firstly produced a simple rule base that enables improved interpretability of variation in rainfall rate. The given fuzzy classification rules were then utilized to generate a neuro-fuzzy inference system in order to predict rainfall variation. This approach measured the accuracy of the prediction model according to the root mean square error (RMSE) estimation. The models resulted low values of the RMSE indicated that the prediction models are reliable in representing the recent inter-annual variation of the wet season tropical rainfall.

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Rainfall-runoff modelling using adaptive neuro-fuzzy systems

Cited by 46 publications

References 5 publications

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique

Real-time forecasting of typhoon inundation extent in a partially-gauged area through the integration of ARX-based models and a geographic information system

Neuro-Fuzzy Approaches for Modeling the Wet Season Tropical Rainfall

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