Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

Fırat, Mahmut; Turan, Mustafa Erkan

doi:10.1111/j.1747-6593.2008.00162.x

Cited by 31 publications

(16 citation statements)

References 25 publications

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“…It can be adopted as a useful source of reference in determining loading of water supply plants. In [10], a monthly river flow forecasting is performed and the best-fit forecasting model is determined. The long-term water flow forecasting is performed through an adaptive neuro-fuzzy inference system using river flow data during three years.…”

Section: Introductionmentioning

confidence: 99%

“…The third is achieving cost reductions such as reducing electricity consumption by applying automated remote control technologies such as reducing electricity consumption. Among them, cost saving can be achieved by reducing the unit costs of production, which is the most important method in terms of management because of advanced automation technologies [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting

Seok

Kim

Lee

et al. 2014

Int. J. Control Autom. Syst.

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A short-term hourly water demand forecasting algorithm is needed in order to ensure a stable and safe supply of water. Unlike daily or monthly water demand forecasting, there are a large amount of fluctuation of hourly water demand. Hourly water demand is affected by short time period and abnormal data caused by the sensor, communication, and water treatment plant problems. An effective refinement method that detects and corrects the abnormal data among the historical data is needed to achieve accurate and practical hourly water demand forecasting. In this paper, we suggest an abnormal data refinement out of a confidence interval (ADR-CI) method and an error percentage correction (EPC) method. These methods try to distribute and revise the incoming hourly water demand and past water demand data. The proposed methods are verified by the experiments in a real water supply plant during a year.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting

Seok

Kim

Lee

et al. 2014

Int. J. Control Autom. Syst.

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show abstract

“…Moreover, the influence of these variables and many of their combinations in generating streamflow is an extremely complex physical process especially due to the data collection of multiple inputs and parameters, which vary in space and time (Akhtar et al, 2009), and are not clearly understood (Zhang and Govindaraju, 2000). Owing to the complexity of this process, most conventional approaches are unable to provide sufficiently accurate and reliable results (Firat and Turan, 2010).…”

Section: Introductionmentioning

confidence: 99%

River flow time series using least squares support vector machines

Samsudin

Saad

Shabri

2011

Hydrol. Earth Syst. Sci.

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Abstract. This paper proposes a novel hybrid forecasting model known as GLSSVM, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM). The GMDH is used to determine the useful input variables which work as the time series forecasting for the LSSVM model. Monthly river flow data from two stations, the Selangor and Bernam rivers in Selangor state of Peninsular Malaysia were taken into consideration in the development of this hybrid model. The performance of this model was compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and LSSVM models using the long term observations of monthly river flow discharge. The root mean square error (RMSE) and coefficient of correlation (R) are used to evaluate the models' performances. In both cases, the new hybrid model has been found to provide more accurate flow forecasts compared to the other models. The results of the comparison indicate that the new hybrid model is a useful tool and a promising new method for river flow forecasting.

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“…However, these models are too complex and demanding. In some cases, conceptual models cannot predict SF accurately and reliably given the lack of required data, especially in developing countries [6], furthermore, the physical process is complicated by the gathering of data on multiple model variables that vary spatially and temporally [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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Abstract. Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively.

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Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

Cited by 31 publications

References 25 publications

Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting

Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting

River flow time series using least squares support vector machines

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

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