A comparison between artificial neural network method and nonlinear regression method to estimate the missing hydrometric data

Bahrami, Jamil; Kavianpour, Mohammad Reza; Abdi, M. S.; Telvari, Abdoulrasoul; Abbaspour, Karim C.; Rouzkhash, B.

doi:10.2166/hydro.2010.069

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…For this reason, a technique capable of generating realistic simulations of the missing data, reflecting the complex structures of the signal, and possibly making use of auxiliary information, is needed. 10 Many different approaches have been proposed for time-series gap filling in earth sciences: techniques based on mean diurnal variation or regression Falge et al (2001); Moffat et al (2007), autoregression Bennis et al (1997); Wang (2008), singular spectrum analysis Schoellhamer (2001) ;Kondrashov et al (2014), self-organizing maps Wang (2003); Lamrini et al (2011), look-15 up tables Bamberger et al (2014), rough sets Dumedah et al (2014), and artificial neural networks, widely used in recent years Aminian and Ameri (2005); Dastorani et al (2009);Diamantopoulou (2010); Nkuna and Odiyo (2011); Bahrami et al (2011);Nourani et al (2012); Dumedah et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Missing data simulation inside flow rate time-series using multiple-point statistics

Oriani

Borghi

Straubhaar

et al. 2016

Environmental Modelling & Software

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Section: Introductionmentioning

confidence: 99%

Missing data simulation inside flow rate time-series using multiple-point statistics

Oriani

Borghi

Straubhaar

et al. 2016

Environmental Modelling & Software

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“…The missing hydrometric data were investigated and predicted by Bahrami et al using the artificial neural network method and nonlinear regression method. Based on some performance indices, the results showed that ANN could be applied as an appropriate tool for predicting the missing data [22]. Based on intact rocks' behavior and some carbonate rocks' properties, the relationship between slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity was evaluated by Yagiz et al using artificial neural network and nonlinear regression techniques.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of the Production Rate of Chain Saw Machine using the Multilayer Perceptron (MLP) Neural Network

et al. 2018

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The production rate in rock cutting machines is one of the most influential parameters in designing and planning procedures. Complete understanding of the production rate of cutting machines help experts and owners of this industry to predict the production expenses. Therefore, the present study predicts the production rate of the chain saw machine in dimensional stone quarries. In this research, the method of artificial neural networks was used for modeling and predicting the production rate. In addition, in this modeling, 98 data were collected from the results obtained from field studies on 7 carbonate rock samples as the dataset. Four important parameters, including uniaxial compressive strength (UCS), Los Angeles abrasion (LAA) Test, equivalent quartz content (Qs), and Schmidt Hammer (Sch) were considered as input data and the production rate was considered as the output data. The model was evaluated by the performance indices for artificial neural networks, including the value account for (VAF), root mean square error (RMSE), and coefficient of determination (R2). For simulation, 10 models were created and evaluated. Finally, the best model, i.e. model No. 3, was selected with a 4 × 3 × 1 structure, including 4 input neurons, 3 neurons in the hidden layer and 1 output neuron. The results obtained from the model’s performance indices show that a very appropriate prediction has been done for determining the production rate of the chain saw machine by artificial neural networks.

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“…ANNs have been extensively applied in the past decade for estimating and forecasting hydrological variables [39,40]. In this work, the MLP method has been applied.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Comparative Analysis of Spatial Interpolation Methods in the Mediterranean Area: Application to Temperature in Sicily

Piazza

Conti

Viola

et al. 2015

Water

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An exhaustive comparison among different spatial interpolation algorithms was carried out in order to derive annual and monthly air temperature maps for Sicily (Italy). Deterministic, data-driven and geostatistics algorithms were used, in some cases adding the elevation information and other physiographic variables to improve the performance of interpolation techniques and the reconstruction of the air temperature field. The dataset is given by air temperature data coming from 84 stations spread around the island of Sicily. The interpolation algorithms were optimized by using a subset of the available dataset, while the remaining subset was used to validate the results in terms of the accuracy and bias of the estimates. Validation results indicate that univariate methods, which neglect the information from physiographic variables, significantly entail the largest errors, while performances improve when such parameters are taken into account. The best results at the annual scale have been obtained using the the ordinary kriging of residuals from linear regression and from the artificial neural network algorithm, while, at the monthly scale, a Fourier-series Water 2015, 7 1867 algorithm has been used to downscale mean annual temperature to reproduce monthly values in the annual cycle.

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A comparison between artificial neural network method and nonlinear regression method to estimate the missing hydrometric data

Cited by 8 publications

References 27 publications

Missing data simulation inside flow rate time-series using multiple-point statistics

Missing data simulation inside flow rate time-series using multiple-point statistics

Prediction of the Production Rate of Chain Saw Machine using the Multilayer Perceptron (MLP) Neural Network

Comparative Analysis of Spatial Interpolation Methods in the Mediterranean Area: Application to Temperature in Sicily

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