Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system

Fırat, Mahmut; Güngör, Mahmud

doi:10.1002/hyp.6812

Cited by 85 publications

(33 citation statements)

References 31 publications

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“…It is clearly seen that the reported RMSE performance is statically significant by considering the Diebold and Mariano [39] perspective stat ranging of −1.2 to +1.0. 2 …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…This model presents the parsimony and produces the rational results for linear time series data. In recent years, the use of statistical forecasting approaches has been challenged by the artificial intelligence approaches [1][2][3][4][5]. All the studies reviewed so far, there are limits to how far the statistical forecasting approaches can be applied.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

Karia¹,

Bujang²,

Ahmad³

2013

AJOR

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An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.

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“…It is clearly seen that the reported RMSE performance is statically significant by considering the Diebold and Mariano [39] perspective stat ranging of −1.2 to +1.0. 2 …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

Karia¹,

Bujang²,

Ahmad³

2013

AJOR

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“…Adaptative Neuro-Fuzzy Inference System (ANFIS) is one of the hybrid neuro-fuzzy inference expert systems and it works in Takagi-Sugeno-type fuzzy inference system. The technique provide a method for the fuzzy modeling procedure to learn information about a data set, in order to compute the membership function parameters that best allow the associated fuzzy inference system to track the given input/output data [12][13]. This learning method works in a manner similar to that of neural networks.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)

Dwivedi¹,

Niranjan²,

Sahu³

2013

IJCA

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Today, Sales forecasting plays a key role for each business in this competitive environment. The forecasting of sales data in automobile industry has become a primary concern to predict the accuracy in future sales. This work addresses the problem of monthly sales forecasting in automobile industry (maruti car). The data set is based on monthly sales (past 5 year data from 2008 to 2012). Primarily, we used two forecasting methods namely Moving Average and Exponential smoothing to forecast the past data set and then we use these forecasted values as a input for ANFIS (Adaptive Neuro Fuzzy Inference System). Here, MA and ES forecasted values used as input variable for ANFIS to obtain the final accurate sales forecast.Finally we compare our model with two other forecasting models: ANN (Artificial Neural Network) and Linear Regression. Empirical results demonstrate that the ANFIS model gives better results out than other two models.

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“…Most studies suggest that the ratio of splitting for training and testing should be [70:30, 80:20, or 90:10]. The selection of the ratio could be based on the particular problem under consideration (Zhang et al, 1998;Firat, 2007;Kisi, 2008;Wang et al, 2009). Before the training process begins, data normalisation is often performed.…”

Section: The Study Area and Datamentioning

confidence: 99%

“…The empirical results from three well-known real datasets showed that the proposed input variables can be an effective way to improve the forecasting accuracy achieved by ANN. The use of input variables from the data values of previous time series and the optimum number of input variables determined by trial and error has been reported by Firat (2007Firat ( , 2008, Firat and Gungor (2007), Sivapragasam and Liong (2005), Juhos et al (2008), Partal and Kisi (2007), among others.…”

Section: Input Determinationmentioning

confidence: 99%

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

Ismail

Shabri

Samsudin³

2012

Hydrol. Earth Syst. Sci.

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Abstract. Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM model for river flow forecasting. The hybrid model uses the SOM algorithm to cluster the entire dataset into several disjointed clusters, where the monthly river flows data with similar input pattern are grouped together from a high dimensional input space onto a low dimensional output layer. By doing this, the data with similar input patterns will be mapped to neighbouring neurons in the SOM's output layer. After the dataset has been decomposed into several disjointed clusters, an individual LSSVM is applied to forecast the river flow. The feasibility of this proposed model is evaluated with respect to the actual river flow data from the Bernam River located in Selangor, Malaysia. The performance of the SOM-LSSVM was compared with other single models such as ARIMA, ANN and LSSVM. The performance of these models was then evaluated using various performance indicators. The experimental results show that the SOM-LSSVM model outperforms the other models and performs better than ANN, LSSVM as well as ARIMA for river flow forecasting. It also indicates that the proposed model can forecast more precisely, and provides a promising alternative technique for river flow forecasting.

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Hydrological time‐series modelling using an adaptive neuro‐fuzzy inference system

Cited by 85 publications

References 31 publications

Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches

A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)

A hybrid model of self organizing maps and least square support vector machine for river flow forecasting

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