Evapotranspiration Calculation for South Carolina, USA and Creation Different ANFIS
            Models for ET Estimation.

Kaya, Yunus Ziya; Taşar, Bestami

doi:10.24193/awc2019_22

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…A typical fuzzy system is hybridised with an ANN to design a neuro-fuzzy system that combines the benefits of fuzzy logic and artificial neural networks. It is evident that, compared to a standalone fuzzy system or an ANN, hybrid neuro-fuzzy systems, especially adaptive neuro-fuzzy inference systems (ANFIS), confirmed better accuracy for modeling ( Ladlani et al, 2014 ; Kisi et al, 2015 ; Patil & Deka, 2017 ; Salih et al, 2019 ; Kaya & Taşar, 2019 ; Üneş, Kaya & Mamak, 2020 ; Güzel et al, 2023 ). However, the ANFIS models suffer from several significant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

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Section: Introductionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

View full text Add to dashboard Cite

show abstract

“…For these reasons, the estimation of sediment amount is needed in the design of water structures. In the last years, the artificial intelligence approaches are a technique widely used in water resources engineering and hydrology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Thangaraj and Kalaivani [18] estimated the water level in the river using support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of River Sediment Amount using Machine Model

Üneş¹,

Karaeminoğullari²,

Taşar³

2020

IJEAB

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Accurate estimation of sediments is important in river structures. The amount of suspended sediments is mostly determined by measurements from observation stations, sediment key curve, artificial intelligence modeling methods. In this study, the estimation of the sediments content was performed by using hydro-meteorological parameters such as river flow, air temperature and precipitation measured between 2011 -2017 at Omaha Station in Nebraska. For the estimation of sediments amount, Support Vector Machines (SVM) and Generalized Regression Neural Network (GRNN) methods were used. These models were compared by using correlation coefficient (R), mean absolute error (MAE) and root of mean square errors (RMSE). When the measurement and model results were compared, SVM and GRNN models gave consistent results in the estimation of sediments content in rivers. Nevertheless, the SVM method showed slightly better correlation and lower error performance than the GRNN method.

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“…it becomes a valuable tool for complex scenarios, which are difficult to define by methods.. Recently, artificial intelligence methods have begun to be frequently used in modeling the rainfallrunoff [1][2], suspended sediment [3][4][5][6], dam reservoir level [7][8][9][10], density flow plunging [11], dam reservoir volume [12][13][14][15], sand bar crest [16], evaporation [17][18], and groundwater level [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Ground Water Level Estimation for Dörtyol region in HATAY

Üneş¹,

Maruf²,

Taşar³

2019

IJEAB

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Accurate and reliable estimation of groundwater level is important for the development and management of water resources. In this study, models of adaptive neuro-fuzzy inference system (ANFIS) with multiple linear regression (MLR) method and its performance in predicting groundwater level were investigated. As a field of application, it was applied for General Directorate of State Hydraulic Works (DSİ) 5512 well of Dörtyol region of Hatay province. In the study, 147-month data sets between 2000 and 2015, including hydrological parameters such as Precipitation (P), average air temperature (T), relative humidity (RH), wind speed (W) groundwater level (GWL) time series, predict the groundwater level used. The determinant coefficient (R 2 ), mean square error (MSE) and mean absolute error (MAE) were used as the statistical performance evaluation criteria. As a result of this study, MLR and ANFIS models performed well for GWL estimation. In particular, the ANFIS model yielded better results than the MLR model.

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Evapotranspiration Calculation for South Carolina, USA and Creation Different ANFIS Models for ET Estimation.

Cited by 6 publications

References 12 publications

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Forecasting of River Sediment Amount using Machine Model

Ground Water Level Estimation for Dörtyol region in HATAY

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