Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship

Lohani, A. K.; Goel, N. K.; Bhatia, Karamjit

doi:10.1016/j.jhydrol.2006.05.007

Cited by 118 publications

(55 citation statements)

References 31 publications

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“…We used neuro-fuzzy network to simulate groundwater quality and also GIS was used to increase the accuracy and rapidness of modeling and monitoring of the results of the neuro-fuzzy network in large-scale. Previous researches results show the high performance of the neurofuzzy network with the structure of the Takagi-SugenoKang (TSK) model in hydrologic simulations as well (Jang et al 1997;Jacquin and Shamseldin 2006;Lohani et al 2006;Talei et al 2010;Heydari and Talaee 2011). In terrain and optimization stages, we found that TSK model is the best structure for neuro-fuzzy network in the groundwater quality simulation.…”

Section: Discussionmentioning

confidence: 62%

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

2016

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The process of water quality testing is money/-time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CAN-FIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the preprocessor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

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Section: Discussionmentioning

confidence: 62%

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

2016

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“…They further concluded that adding more complexity to model structure cannot compensate the need of including physically essential variables which account for hydraulic behaviour of low gradient streams. Lohani et al (2006) modelled the rating curve using the Takagi-Sugeno (TS) fuzzy inference. Data sets of five Indian gauging sites as well as a hypothetical looped rating curve were used for the study.…”

Section: Materials and Methodologymentioning

confidence: 99%

Application of Data Driven Techniques in Discharge Rating Curve - An Overview

Zakwan

Muzzammil

Alam

2017

Aquademia: Water, Environment and Technology

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Establishing a reliable rating curve is an integral part of water resource engineering. The present paper is a review of data driven techniques used for modelling the stage discharge relationship over the last two decades. Over the year's several data driven techniques such as ANN, SVM, MT, fuzzy logic and GA have been applied by researchers in quest of arriving at a reliable technique to model the stage discharge relationship. Although, during this period several techniques have been used to establish the stage discharge relationship but the application of modern optimization tools such as ACO, PSO or PS has remained limited in this field. Therefore, application of modern optimization techniques and their comparison with existing literature would be appreciated in near future.

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“…Clustering included the selection of type, number, and linguistic value of membership function. Subtractive clustering proposed by Chiu [46] is applied because it finds out the optimum cluster centres (optimal parameters of membership functions) and consequently the optimum fuzzy model [47]. It calculates a measure of the likelihood of each data point as the cluster centre based on the density of surrounding data points.…”

Section: Methodsmentioning

confidence: 99%

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

Azizi

Ali

Ping

2015

The South African Journal of Industrial Engineering

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Production throughput measures the performance and behaviour of a production system. Production throughput modelling is complex because of uncertainties in the production line. This study examined the potential application of the adaptive neuro-fuzzy inference system (ANFIS) to modelling the throughput of production under five significant production uncertainties: scrap, setup time, break time, demand, and lead time of manufacturing. The effects of these uncertainties on the production of floor tiles were studied by performing 104 observations on the production uncertainties over 104 weeks, based on a weekly production plan in a tile manufacturing industry. The results of the ANFIS model were compared with the multiple linear regression (MLR) model. The results showed that the ANFIS model was capable of forecasting production throughput under uncertainty with higher accuracy than was the MLR model, indicated by an R-squared of 98 per cent. OPSOMMINGProduksie deurset meet die vertoning en gedrag van 'n produksiesisteem. Produksie deurset modellering is ingewikkeld as gevolg van die onsekerhede in die produksielyn. Hierdie studie ondersoek die toepassing van die aanpasbare neuro-wasige afleidingsisteem om die deurset van produksie onderhewe aan vyf noemenswaardige produksie onsekerhede, naamlik skroot, opstel tyd, breek tyd, aanvraag en die vervaardiging leityd. Die effek van hierdie onsekerhede op die vervaardiging van vloerteëls is ondersoek deur 104 weeklikse observasies op die produksie onsekerhede oor 'n tydperk van 104 weke te neem. Die resultate van die model is vergelyk met 'n meervoudige lineêre regressie model. Die resultate toon dat die aanpasbare neuro-wasige afleidingsisteem in staat was om produksie deurset onderhewe aan onsekerheid te voorspel met 'n hoër akkuraatheid as die meervoudige lineêre regressie model. Dit word aangedui met 'n bepaalheidskoëffisient van 98 persent.

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Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship

Cited by 118 publications

References 31 publications

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

Application of Data Driven Techniques in Discharge Rating Curve - An Overview

Modelling Production Uncertainties Using the Adaptive Neuro-Fuzzy Inference System

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