A neuro-fuzzy computing technique for modeling hydrological time series

Nayak, P. C.; Sudheer, K. P.; Rangan, Dhruva; Ramasastri, K. S.

doi:10.1016/j.jhydrol.2003.12.010

Cited by 558 publications

(264 citation statements)

References 33 publications

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“…These blocks are shown schematically in Fig. 4., and an adaptive network has a multilayer feed forward network structure (Nayak et al, 2004). (Jang, 1993).…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

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“…These blocks are shown schematically in Fig. 4., and an adaptive network has a multilayer feed forward network structure (Nayak et al, 2004). (Jang, 1993).…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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“…The approach is based on fuzzy inference systems already used extensively in hydrological and water quality modelling (e.g. Chen et al, 2006;Dixon, 2005;Haberlandt et al, 2002;Jacquin and Shamseldin, 2006;Marce et al, 2004;Nayak et al, 2004). These modelling systems integrate the outputs from a number of sub-models to estimate a single overall output.…”

Section: Estimation Of Nutrients Loads Using Catchment Characteristicsmentioning

confidence: 99%

Derivation of a fuzzy national phosphorus export model using 84 Irish catchments

Nasr¹,

Bruen

2013

Science of The Total Environment

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► Develops a new national phosphorus export model for agricultural catchments in Ireland ► Improves on earlier empirical phosphorus export models by using k-means clustering method for partitioning data ► Uses ANFIS model to predict annual average ortho-phosphorus concentrations using catchment characteristics ► Phosphorus desorption index (PDI) and runoff risk index (RRI) are essential predictors in the model. a b s t r a c t a r t i c l e i n f o Implementation of appropriate management strategies to mitigate diffuse phosphorus (P) pollution at the catchment scale is vitally important for the sustainable development of water resources in Ireland. An important element in the process of implementing such strategies is the prediction of their impacts on P concentrations in a catchment using a reliable mathematical model. In this study, a state-of-the-art adaptive neuro-fuzzy inference system (ANFIS) has been used to develop a new national P model capable of estimating average annual ortho-P concentrations at un-gauged catchments. Data from 84 catchments dominated by diffuse P pollution were used in developing and testing the model. Six different split-sample scenarios were used to partition the total number of the catchments into two sets, one to calibrate and the other to validate the model. The k-means clustering algorithm was used to partition the sets into clusters of catchments with similar features. Then for each scenario and for each cluster case, 11 different models, each of which consists of a linear regression sub-model for each cluster, were formulated by using different input variables selected from among six spatially distributed variables including phosphorus desorption index (PDI), runoff risk index (RRI), geology (GEO), groundwater (GW), land use (LU), and soil (SO). The success of the new approach over the conventional lumped, empirical, modelling approach was evident from the improved results obtained for most of the cases. In addition the results highlighted the importance of using information on PDI and RRI as explanatory input variables to simulate the average annual ortho-P concentrations.

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“…Vernieuwe et al (2005) applied it to the modeling of rainfall-discharge dynamics. Nayak et al (2004) applied it to model hydrologic time series, and reported that ANFIS was superior to ANN and other statistical methods.…”

Section: Fmentioning

confidence: 99%

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

Tesfamariam

Najjaran

2007

J. Mater. Civ. Eng.

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A neuro-fuzzy computing technique for modeling hydrological time series

Cited by 558 publications

References 33 publications

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Derivation of a fuzzy national phosphorus export model using 84 Irish catchments

Adaptive Network–Fuzzy Inferencing to Estimate Concrete Strength Using Mix Design

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