Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support
            Vector Machines

Taşar, Bestami; Üneş, Fatih; Varçin, Hakan

doi:10.24193/awc2019_24

Cited by 8 publications

(4 citation statements)

References 22 publications

(15 reference statements)

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“…Over the past decade, ML techniques have gained immense popularity in hydrology research. ML techniques have been successfully implemented for various hydrological applications for example flood modeling (Mosavi et al, 2018;Janizadeh et al, 2019) drought assessment (Feng et al, 2019;Shamshirband et al, 2020;Rhee and Im, 2017), water demand studies (Villarin and Rodriguez-Galiano, 2019;Xenochristou et al, 2021) rainfall modeling (Cramer et al, 2017;Basha et al, 2020), runoff modeling (Kumar et al, 2019;Tașar et al, 2019). Some of the ML models specifically used for rainfall-runoff modeling include ANN (Sudheer et al, 2002;Srinivasulu and Jain, 2006), adaptive neurofuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) (Nourani and Komasi, 2013;Talei et al, 2010), multivariate adaptive regression splines model (MARS) (Sharda et al, 2008) and M5 model tree (M5Tree) (Adnan et al, 2021;Nourani et al, 2019), support vector regression (SVR) (Hosseini and Mahjouri, 2016;Sedighi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India

Ojha¹,

Singh²,

Roshni³

2023

Int. j. adv. appl. sci.

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Machine learning (ML) models have emerged as potential methods for rainfall-runoff modeling in recent decades. The appeal of ML models for such applications is owing to their competitive performance when compared to alternative approaches, ease of application, and lack of rigorous distributional assumptions, among other attributes. Despite the promising results, most ML models for rainfall-runoff applications have been limited to areas where rainfall is the primary source of runoff. The potential of Random Forest (RF), a popular ML method, for rainfall-runoff prediction in the Punpun river basin, India, is investigated in this paper. The correlation coefficient (R), Root mean squared error (RMSE), Mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) are four statistical metrics used to compare RF performance to that of alternative ML models. Model evaluation metrics indicate that RF outperforms all others. In the RF model, we got the best NSE score of 0.795. These findings offer new perspectives on how to apply RF-based rainfall-runoff modeling effectively.

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Section: Introductionmentioning

confidence: 99%

Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India

Ojha¹,

Singh²,

Roshni³

2023

Int. j. adv. appl. sci.

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“…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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Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines

Cited by 8 publications

References 22 publications

Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India

Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India

Forecasting of River Sediment Amount using Machine Model

Ground Water Level Estimation for Dörtyol region in HATAY

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