Adaptive neuro-fuzzy computing technique for suspended sediment estimation

Kisi, Özgür; Haktanır, Tefaruk; Ardıçlıoğlu, Mehmet; Öztürk, Özgür; Yalcin, Ekrem; Uludag, Salih

doi:10.1016/j.advengsoft.2008.06.004

Cited by 99 publications

(38 citation statements)

References 31 publications

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“…Kisi [21] je modelirao koncentraciju lebdećeg nanosa pomoću metoda ANFIS i ANN te je ustanovio da je model ANFIS prikladniji od modela ANN. U radu [22] težište se stavlja na prednosti metode ANFIS u odnosu na modele ANN i SRC (eng. sediment rating curve, krivulja pronosa nanosa) u prognozi mjesečnih lebdećih nanosa na stanicama Kuylus i Salur Koprusu u slivu Kizilirmak u Turskoj.…”

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Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

2015

JCE

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Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa U radu se istražuju mogućnosti koje pruža prilagodljivi sustav neizrazitog zaključivanja zasnovanog na neuronskoj mreži (ANFIS) u predviđanju dnevnih količina lebdećeg nanosa koje je obavljeno na četiri stanice u SAD-u. U tu su svrhu izrađeni modeli s različitim kombinacijama ulaznih podataka kao osnova za određivanje najbolje moguće strukture. Dobiveni rezultati pokazuju da se najbolji model ANFIS ponaša bolje od modela SRC s obzirom na dobivene vrijednosti RMSE, MBE i R2. Rezultati također pokazuju da se pomoću modela ANFIS može pojednostavniti modeliranje nelinearne dinamike složenih sustava. Assessing ANFIS accuracy in estimation of suspended sediments Capabilities offered by an adaptive neuro-fuzzy inference system (ANFIS) in the estimation of daily sediment loads at four stations in the USA, are explored in the paper. For this purpose, models with various input combinations of data sets were constructed to enable identification of the best possible structure. The results show that the best ANFIS model exhibits better performance compared to the SRC model, in terms of the RMSE, MBE and R2 values. The results also indicate that the ANFIS model can be applied to facilitate modelling of nonlinear dynamics of complex systems.

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Section: Uvodunclassified

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

2015

JCE

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“…For instance, neuro-fuzzy has been used successfully for prediction of flow through rock-fill dams (Heydari and Talaee 2011), river flow (Nayak et al 2004(Nayak et al , 2005Pramanik and Panda 2009;Kisi 2010), suspended sediment estimation (Kisi et al 2008;Cobaner et al 2009;Mirbagheri et al 2010, groundwater vulnerability (Dixon 2005, groundwater quality problems (Lu and Lo 2002;Zhou et al 2007;Hass et al 2012;Rapantova et al 2012;Jang and Chen 2015), daily evaporation (Dogan et al 2010;KarimiGooghari 2012) and rainfall-runoff modeling (Chang and Chen 2001;Gautam and Holz 2001;Xiong et al 2001;Jacquin and Shamseldin 2006). However, little research has been undertaken to study the problem of groundwater quality using ANN and GIS.…”

Section: Introductionmentioning

confidence: 99%

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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“…However, these methods fall short of capturing hydrological responses to significant changes in physiographical (e.g., land-use/land-cover) and climatological (e.g., climate change) characteristics of a watershed. Artificial neuro-fuzzy inference systems (ANFIS) and Bayesian regression methods have received more attention in recent years in the water resources field due to their ability to model sophisticated non-linear systems such as streamflow and contaminant transport [25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow

Hamaamin

Nejadhashemi

Zhang

et al. 2016

Water

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Accurate and efficient estimation of streamflow in a watershed's tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost-effective approach to water resources planning and management. Two data-driven methods, Bayesian regression and adaptive neuro-fuzzy inference system (ANFIS), were tested separately as a faster alternative to a calibrated and validated Soil and Water Assessment Tool (SWAT) model to predict streamflow in the Saginaw River Watershed of Michigan. For the data-driven modeling process, four structures were assumed and tested: general, temporal, spatial, and spatiotemporal. Results showed that both Bayesian regression and ANFIS can replicate global (watershed) and local (subbasin) results similar to a calibrated SWAT model. At the global level, Bayesian regression and ANFIS model performance were satisfactory based on Nash-Sutcliffe efficiencies of 0.99 and 0.97, respectively. At the subbasin level, Bayesian regression and ANFIS models were satisfactory for 155 and 151 subbasins out of 155 subbasins, respectively. Overall, the most accurate method was a spatiotemporal Bayesian regression model that outperformed other models at global and local scales. However, all ANFIS models performed satisfactory at both scales.

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Adaptive neuro-fuzzy computing technique for suspended sediment estimation

Cited by 99 publications

References 31 publications

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

Procjena točnosti ANFIS-a u prognoziranju lebdećeg nanosa

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

Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow

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