A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction

Maiti, Saumen; Tiwari, Rohit

doi:10.1007/s12665-013-2702-7

Cited by 90 publications

(33 citation statements)

References 40 publications

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“…Artificial neural network (ANN) is a well-known branch of soft computing (Alavi et al 2010). This technique has been successfully employed to solve problems in civil engineering field (e.g., Kayadelen et al 2009;Günaydın 2009;Kolay et al 2010;Das et al 2010;Yilmaz 2010a, b; Akgun and Türk 2010; Kaunda et al 2010;Das et al 2011a, b, c;Mert et al 2011;Mollahasani et al 2011;Yilmaz et al 2012;Sattari et al 2012;Tasdemir et al 2013;Seker 2012, 2013;Isik and Ozden 2013;Alkhasawneh et al 2014;Wu et al 2013;Maiti and Tiwari 2014;Park et al 2013;Ceryan et al 2013;Manouchehrian et al 2014). Besides, ANN has been used to predict the bearing capacity of shallow foundations resting on soil layers (Soleimanbeigi and Hataf 2005;Padmini et al 2008;Kuo et al 2009;Kalinli et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

et al. 2014

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A major concern in the design of foundations is to achieve a precise estimation of bearing capacity of the underlying soil or rock mass. The present study proposes a new design equation for the prediction of the bearing capacity of shallow foundations on rock masses utilizing artificial neural network (ANN). The bearing capacity is formulated in terms of rock mass rating, unconfined compressive strength of rock, ratio of joint spacing to foundation width, and angle of internal friction for the rock mass. Further, a conventional calculation procedure is proposed based on the fixed connection weights and bias factors of the best ANN structure. A comprehensive database of rock socket, centrifuge rock socket, plate load, and large-scaled footing load test results is used for the model development. Sensitivity and parametric analyses are conducted and discussed. The results clearly demonstrate the acceptable performance of the derived model for estimating the bearing capacity of shallow foundations. The proposed prediction equation has a notably better performance than the traditional equations.

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Section: Introductionmentioning

confidence: 99%

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

et al. 2014

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“…Another advantage of SCG is that it does not involve any user-dependent parameters. The SCG method has been used to solve problems in different research domains such as prediction of groundwater level [51] and telemarketing success [52].…”

Section: Model Building and Validationmentioning

confidence: 99%

A Novel Preprocessing Scheme to Improve the Prediction of Sand Fraction From Seismic Attributes Using Neural Networks

Chaki

Routray

Mohanty³

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a novel preprocessing scheme to improve the prediction of sand fraction from multiple seismic attributes such as seismic impedance, amplitude, and frequency using machine learning and information filtering. The available well logs along with the three-dimensional (3-D) seismic data have been used to benchmark the proposed preprocessing stage using a methodology that primarily consists of three steps: 1) preprocessing; 2) training; and 3) postprocessing. An artificial neural network (ANN) with conjugate-gradient learning algorithm has been used to model the sand fraction. The available sand fraction data from the high-resolution well logs have far more information content than the low-resolution seismic attributes. Therefore, regularization schemes based on Fourier transform (FT), wavelet decomposition (WD), and empirical mode decomposition (EMD) have been proposed to shape the high-resolution sand fraction data for effective machine learning. The input data sets have been segregated into training, testing, and validation sets. The test results are primarily used to check different network structures and activation function performances. Once the network passes the testing phase with an acceptable performance in terms of the selected evaluators, the validation phase follows. In the validation stage, the prediction model is tested against unseen data. The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume. Finally, a postprocessing scheme using 3-D spatial filtering is implemented for smoothing the sand fraction in the volume. Prediction of lithological properties using this framework is helpful for reservoir characterization (RC).Index Terms-Artificial neural network (ANN), empirical mode decomposition (EMD), entropy, Fourier transform (FT), normalized mutual information (NMI), preprocessing, regularization, reservoir characterization (RC), sand fraction (SF), threedimensional (3-D) median filtering, wavelets.

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“…Recently, many successful applications of ANFIS in the field of water resources were reported. For example, [18] applied the ANFIS model to modeling groundwater level fluctuation using hydro meteorological data (precipitation and temperature). They compared the results from the ANFIS model with multiple layer perceptron optimized by two different approach i.e.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

Al-Mukhtar

2018

MATEC Web Conf.

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Modeling of suspended sediment load in rivers has a major role in a proper management of water resources. Artificial intelligence has been identified as an efficient way to model the complex nonlinear hydrological relationship. In this study, Adaptive Neuro Fuzzy Inference System (ANFIS), in addition to two different kinds of Artificial Neural Network (ANN) i.e. feedforward and radial basis networks were used and compared to model the suspended sediment load (SSL) in Tigris River-Baghdad using the streamflow discharge as input. To this end, an intermittent data of SSL and streamflow were collected over the period 1962-1981 from Sarai station in Baghdad. 70 % of these data was used to calibrate (train) the networks and the remaining 30% for the validation (test). The coefficient of determination (R2), root mean square error (RMSE), and Nash and Sutcliffe model efficiency coefficient (NSE) were used to judge whether the observed and modelled data belong to the same distribution. Results revealed that the ANFIS model outperform the other methods. R2, RMSE, and NSE of ANFIS during the calibration phase were equal to 0.58, 75617, and 0.58, respectively and during the validation were 0.72, 27944, and 0.59, respectively. Therefore, ANFIS approach is recommended to estimate the river suspended sediment load.

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A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction

Cited by 90 publications

References 40 publications

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

A Novel Preprocessing Scheme to Improve the Prediction of Sand Fraction From Seismic Attributes Using Neural Networks

Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

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