Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

Aqil, Muhammad; Kita, Itsuro; Yano, Akira; Nishiyama, Soichi

doi:10.1016/j.jenvman.2006.09.009

Cited by 105 publications

(53 citation statements)

References 15 publications

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“…Table 1 In ANN models, the selection of appropriate input variables is critical for a successful modeling. Some researchers (Sudheer et al 2002;Aqil et al 2007;Bilgili et al 2007) have demonstrated the computation of statistical analysis, such as correlation as well as cross-, auto-, and partial auto-correlation, to determine the appropriate input variables.…”

Section: Resultsmentioning

confidence: 99%

Caspian Sea level prediction using satellite altimetry by artificial neural networks

Imani

You

Kuo

2013

Int. J. Environ. Sci. Technol.

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The demand for accurate predictions of sea level fluctuations in coastal management and ship navigation activities is increasing. To meet such demand, accessible high-quality data and proper modeling process are critically required. This study focuses on developing and validating a neural methodology applicable to the shortterm forecast of the Caspian Sea level. The input and output data sets used contain two time series obtained from Topex/Poseidon and Jason-1 satellite altimetry missions from 1993 to 2008. The forecast is performed by multilayer perceptron network, radial basis function, and generalized regression neural networks. Several tests of different artificial neural network (ANN) architectures and learning algorithms are carried out as alternative methods to the conventional models to assess their applicability for estimating Caspian Sea level anomalies. The results derived from the ANN are compared with observed sea level values and with the forecasts calculated by a routine autoregressive moving average (ARMA) model. Different ANNs satisfactorily provide reliable results for the short-term prediction of Caspian Sea level anomalies. The root mean square errors of the differences between observations and predictions from artificial intelligence approaches can be significantly reduced by about 50 % compared with ARMA techniques.

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Section: Resultsmentioning

confidence: 99%

Caspian Sea level prediction using satellite altimetry by artificial neural networks

Imani

You

Kuo

2013

Int. J. Environ. Sci. Technol.

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“…Lastly, the final output as the weighted average of all rule outputs (aggregation) was calculated in the fifth layer. The NF parameters and membership function parameters were estimated using the hybrid algorithm, which is a combination of the gradient descent and least-squares method (Aqil et al 2007;Akrami et al 2013). …”

Section: Neuro-fuzzy (Nf)mentioning

confidence: 99%

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Tayfur

Nadiri

Moghaddam

2014

Water Resour Manage

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Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.

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“…Artificial neural network (ANN) techniques are popular methods capable of identifying correlated patterns between the input data and the corresponding target values. Therefore, they have been widely and successfully applied in hydrological modeling systems (Abrahart and Kneale, 1997;Dawson and Wilby, 1998;Imrie et al, 2000;Baratti et al, 2003;Castellano-Méndez et al, 2004;Riad et al, 2004;Valença et al, 2005;Aqil et al, 2007). Here, the MLP method, as one of the most popular ANN architectures for hydrological simulations (Castellano-Méndez et al, 2004), was chosen for streamflow prediction and for comparing its prediction ability with the GP method.…”

Section: Evolutionary Modeling With Minimal Datasetsmentioning

confidence: 99%

Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

Wang

et al. 2010

Environmental Engineering Science

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A large dataset is generally needed when modeling hydrological processes. However, for developing countries such as China, datasets are often unavailable in remote areas. An attempt to apply a novel genetic programming (GP) technique was made to model the relationship between streamflow of the West Malian River and the impact of climate change in the northeastern part of China. Available annual streamflow and climatic data were used for training and testing of the GP model. Data from the years between 1982 and 2002 were used for automatic selection of the model relationship. Prediction of the model was undertaken for the period [2003][2004][2005][2006] and the results were compared with measured data. Predicted annual streamflow of the West Malian River agreed with measured data to an acceptable degree of accuracy even with a small amount of dataset. For comparison, a multilayer perceptron method with back propagation algorithm, a gray theory model, and a multiple linear regression model were selected to conduct the prediction with the same dataset. Results showed that the performance of GP method was generally better than other statistical methods such as multilayer perceptron, gray theory model, and multiple linear regression model. Further, the results also showed that the GP method is a useful tool for water resource management, especially in developing countries, to evaluate the potential impacts of climate change on the streamflow when large datasets are unavailable.

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Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

Cited by 105 publications

References 15 publications

Caspian Sea level prediction using satellite altimetry by artificial neural networks

Caspian Sea level prediction using satellite altimetry by artificial neural networks

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Evolutionary Modeling for Streamflow Forecasting with Minimal Datasets: A Case Study in the West Malian River, China

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