Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling

Nourani, Vahid; Baghanam, Aida Hosseini; Adamowski, Jan; Gebremichael, Mekonnen

doi:10.1016/j.jhydrol.2012.10.054

Cited by 156 publications

(49 citation statements)

References 59 publications

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“…In this Figure i, j and k denote input layer, hidden layer and output layer neurons, respectively, and w is the applied weight by the neuron. The explicit expression for an output value of a three layered MLP is given by Nourani et al (2013):…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

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Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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“…In the FFBP algorithm, any input node will be multiplied by a proper weight initially and then will be added by a constant value, which is called bias, and finally will be entered to a predefined activation functions. The explicit expression for an output value of a FFBP network is given by Nourani et al (2013).…”

Section: Overview Of Ffbp Grnn and Rbf Algorithmsmentioning

confidence: 99%

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Mehr

Kahya

ŞAHİN

et al. 2014

Int. J. Environ. Sci. Technol.

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In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Ç oruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root-meansquare error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

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“…Wavelets form the basis of the wavelet transform, which is searched for relationships between the signal or time series and the wavelet function. This calculation is done at different scales of and locally around the time of , which results a wavelet coefficient ( ) which fills up the transform plane (Nourani, Baghanam, Adamows, & Gebremichael, 2013). There are two main types of wavelet transforms: continuous wavelet transform (CWT) and discrete wavelet transform (DWT).…”

Section: Methodology 31 Wavelet Transformmentioning

confidence: 99%

“…For a discrete time series, , the dyadic wavelet transform becomes (Nourani, Baghanam, Adamows, & Gebremichael, 2013 Figure-2 illustrates some of the commonly used wavelet functions and schematic multiresolution decomposition of discrete wavelet transform. Daubechies functions are the most popular functions that widely used to solve a broad range of problems and represent the foundations of wavelet signal processing that used in numerous applications (Soman, Ramachandran, & Resmi, 2011).…”

Section: Methodology 31 Wavelet Transformmentioning

confidence: 99%

Hydrological Time Series Forecasting Using Anfis Models With Aid of Wavelet Transform

Ibrahim

Mustafa

Rashid

2017

juod

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The precise and accurate models of hydrological time series that are embedded with high complexity, nonstationarity, and non-linearity in both spatial and temporal scales can provide important information for decision-making in water resources management and environmental related issues. Hybrid wavelet transform (WT) and adaptive neuro-fuzzy inference system (ANFIS) has been used in this study to improve the forecasting capability of ANFIS model by decomposing the time series into sub-time series (approximation and details) using wavelet transform then combining the effective and significant time lags of sub-time series to form a set of input variables. The present study attempts to add the effective and significant time lags of original time series as extra variables to the input variables set. In addition, different combinations of variables, 1-3, from the set of input variables as inputs to the ANFIS model were used to forecast the time series.To examine the potential of the approach for practical applications, the model is applied to forecast, one step-ahead, the monthly data of hydrological time series (rainfall, evaporation, minimum and maximum temperature, average wind speed and reservoir inflow) for Kirkuk, Sulaimani, Dokan and Darbandikhan meteorological stations in Iraq. The best fit models were selected using the coefficient of determination ( ) and root mean square error ( ). Based on the results, the proposed model has high performance in forecasting the monthly minimum and maximum temperature, evaporation and reservoir inflow with values ranged from 0.93 to 0.99 and relatively good performances in forecasting the monthly rainfall and average wind speed with values ranged from 0.77 to 0.93.

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Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling

Cited by 156 publications

References 59 publications

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Successive-station monthly streamflow prediction using different artificial neural network algorithms

Hydrological Time Series Forecasting Using Anfis Models With Aid of Wavelet Transform

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