Modeling soil temperatures at different depths by using three different neural computing techniques

Kisi, Özgür; Tombul, Mustafa; Zounemat‐Kermani, Mohammad

doi:10.1007/s00704-014-1232-x

Cited by 88 publications

(46 citation statements)

References 35 publications

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“…The gradient descent, conjugate gradient, Levenberg-Marquardt, and etc. learning algorithms can be used for training the MLP model (Kisi et al 2015). Radial basis function neural network (RBFNN) Broomhead and Lowe (1988)introduced radial basis function neural networks in late 1980s.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

“…The hidden layer nodes compute the distance between their centers and the point on the input set that corresponds to the input vector. For the pth input pattern X P , the response of the jth hidden layer node q j is of the following form (Fernando and Jayawardena 1998;Kisi 2009;Kisi et al 2015):…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

“…During the training process, the error is measured by the means of square error (MSE). The training process would be repeated for several/numerous times with different smoothing factors until the network is optimized according to the minimum amount of MSE or a pre-defined threshold value (Kisi et al 2015).…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

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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%

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

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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 recent years, several studies have reported that the ANN with its ability to model non‐linear relationships may offer a promising alternative for soil temperature modelling. Although several applications of ANNs for this type of modelling exist (George, ; Mihalakakou, ; Bilgili, ; Ozturk et al , ; Tabari et al , ; Bilgili et al , ; Wu et al , ; Hosseinzadeh Talaee, ; Kim and Singh, ; Kisi et al , ), they have so far been restricted to the research environment. The outcomes of such researches are encouraging, as the ANN method has been found to be very useful in providing important information regarding the non‐linear characteristics of soil temperature and its predictability.…”

Section: Introductionmentioning

confidence: 99%

Short-term forecasting of soil temperature using artificial neural network

2014

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Soil temperature is one of the most important meteorological parameters that plays a critical role in land surface hydrological processes. In the current study, artificial neural network (ANN) models were developed and tested for 1 day ahead soil temperature forecasting at 5, 10, 20, 30, 50 and 100 cm depths. Antecedent soil temperatures plus concurrent and antecedent air temperatures were used as inputs for the ANN models. Soil and air temperature data were collected from two Iranian weather stations located in humid and arid regions for the period 2004-2005. The models' accuracies were evaluated using the Nash-Sutcliffe co-efficient of efficiency, the correlation co-efficient, the root mean square error and the mean bias error between the observed and forecasted soil temperature values. The Nash-Sutcliffe co-efficient of efficiency values >0.94 and correlation co-efficient >0.96 for all the ANN models show that the models can be applied successfully to provide accurate and reliable short-term soil temperature forecasts.

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“…For the temperature of soil modelling authors in paper [24] use three different neural computing techniques (Multi-Layer Perceptron, Radial Basis Neural Networks, and Generalized Regression Neural Networks). In studying the problem of flood forecasting [25] the three neural computing techniques approach was also used (Multi-Layer Perceptron-Neural Networks Model -MLP-NNM, Generalized Regression Neural Networks Model -GRNNM, and Kohonen Self-Organizing Feature Maps-Neural Networks Model -KSOFM-NNM).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Machining Time for CNC Manufacturing Using Neural Computing

Šarić¹,

Šimunović²,

Šimunović³

et al. 2016

Int. j. simul. model.

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An approach to solving the problem of machining time estimation in production of complex products within CNC machining systems is presented in the paper. Heuristic analysis of the process is used to define the attributes of influence to machining time. For the problem of estimating machining time the following "Neural Computing techniques" are used: Back-Propagation Neural Network, Modular Neural Network, Radial Basis Function Neural Network, General Regression Neural Network and Self-Organizing Map Neural Network. Real data from the technological process obtained by measuring are used to design the model used in investigation. The established model is used to carry out the investigation aimed at learning and testing different algorithms of neural networks and the results are given by the RMS error. The best results in the validation phase were achieved by Modular Neural Network (RMSE: 1.89 %) and Back-Propagation Neural Network (RMSE: 2.03 %) while the worst results were achieved by Self-Organizing Map Neural Network (RMSE: 10.05 %).

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Modeling soil temperatures at different depths by using three different neural computing techniques

Cited by 88 publications

References 35 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

Short-term forecasting of soil temperature using artificial neural network

Estimation of Machining Time for CNC Manufacturing Using Neural Computing

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