Estimation of Saturation Percentage of Soil Using Multiple Regression, ANN, and ANFIS Techniques

Aali, Khaled Ahmad; Parsinejad, Masoud; Rahmani, Bijan

doi:10.5539/cis.v2n3p127

Cited by 27 publications

(20 citation statements)

References 13 publications

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“…According to the RMSE and R 2 values (Table 4), selected ANFIS model has a high prediction performance for estimation of VW for each crop. The same results were reported by Kisi and Ozturk (2007) for evapotranspiration estimation and also Aali et al (2009) in estimation of saturation percentage of Soil.…”

Section: Resultssupporting

confidence: 87%

“…ANNs are based on current understanding of biological nervous systems, though much of the biological details are neglected. ANNs are massively parallel systems composed of many processing elements connected by links of variable weights (Aali et al,2009). There are well known networks with specific architecture which are wieldy used in water resources engineering.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…Cross validation mode (checking mode) monitors the error to find the optimal termination point for training and also avoid overtraining. Testing mode was used to determine how accurately the network can simulate input-output relationships (Aali et al, 2009).…”

Section: Fig 1: Architecture Of Mlp Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Ahmadaali¹,

Liaghat²,

Heydari³

et al. 2013

IJCA

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Wheat, barley, sugerbeet, potato, alfalfa, and corn are common crops produced in Iran, which need the most virtual water volume compared to other crops. Determination of the virtual water for these crops would assist in better management of water resources. The main objective of this study is to find out the best technique for estimating and mapping of virtual water. In this research, the virtual water volume was determined by crop water requirement and crop yields using three ANN structures as well as ANFIS technique. Based on RMSE and R 2 the comparison of obtained results predicted through the applied ANNs structures indicate that the RBF outperforms the other models for estimating virtual water for wheat, potato, corn, and barley. Moreover, a comparison between RBF and ANFIS revealed that ANFIS is a promising model, which can be efficient mathematical tool for estimation of crop"s virtual water.

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Section: Resultssupporting

confidence: 87%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Ahmadaali¹,

Liaghat²,

Heydari³

et al. 2013

IJCA

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show abstract

“…As mentioned earlier, a CBR value is a ected by the type of soil and di erent soil properties [6]. Therefore, Multiple Regression Analysis (MRA), a statistical technique allowing us to predict someone's score in one variable on the basis of their scores on several other variables [39], was carried out by using a SPSS 13.0 package to correlate the measured CBR values to the soil properties, namely, dry density ( dry ), relative density (I D ), water content (w), speci c gravity (G s ), coe cient of uniformity (C u ), coe cient of curvature (C c ), and particle shape (R F and S R ). The MRA yielded the following correlations:…”

Section: Resultsmentioning

confidence: 99%

Investigations on factors influencing the CBR value of some Aegean sands

Erzin¹,

Turkoz²,

Tuskan³

et al. 2016

Scientia Iranica

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The California Bearing Ratio (CBR) value of the soils is very important for geotechnical engineering and earth structures. A CBR value is a ected by the soil type and di erent soil properties. With this in view, in this paper, an attempt has been made for investigating the factors that a ect the CBR values of some Aegean sands collected from nine di erent locations in Manisa (Turkey). The sand samples were tested for mineralogy, particle shape and size, and speci c gravity. The CBR tests were then performed on these samples at di erent dry densities to examine the in uence of dry density, relative density, water content, and particle shape and size on the CBR value. Multiple Regression Analysis (MRA) was performed to predict the CBR value of the sands by using the experimental results. Moreover, several performance indices, such as coe cient of correlation and variance account for mean absolute error and root mean square error, were calculated to check the prediction capacity of the proposed MR equation. The obtained indices make it clear that the equation derived from the samples used in this study applies well, with an acceptable accuracy, to the CBR estimation at the preliminary stage of site investigations.

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“…The learning algorithm of ANFIS is a hybrid-learning algorithm where the premise parameters are determined by backpropagation learning algorithm and the consequent parameters are determined by the least mean square method. One of ANFIS applications is a successfully approximation capability to complex nonlinear functions, [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning algorithm with fuzzy approximation for semi Markov decision problems

Kula

Ocaktan

2015

Journal of Intelligent &Amp; Fuzzy Systems

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Real life stochastic problems are generally large-scale, difficult to model, and therefore, suffer from the curses of dimensionality. Such problems cannot be solved by classical optimization methods. This paper presents a reinforcement learning algorithm using a fuzzy inference system, ANFIS to find an approximate solution for semi Markov decision problems (SMDPs). The performance of the developed algorithm is measured and compared to a classical reinforcement algorithm, SMART in a numerical example. Our numerical examples show that the developed algorithm converges significantly faster as the problem size increases and the average cost calculated by the algorithm gets closer to that of SMART as number of epochs used in the developed algorithm is increased. a 1 1,0,0,0 0,1,0,0 1,1,0,0 0,2,0,0 0,0,1,0 0,3,0,0 0,1,1,0 0,4,0,0 0,2,1,0 0,0,0,1 a 2 2,0,0,0 3,0,0,0 2,1,0,0 1,2,0,0 1,0,1,0 1,3,0,0 1,1,1,0 1,4,0,0 0,0,2,0 a 3 4,0,0,0 3,1,0,0 2,2,0,0 2,0,1,0 2,3,0,0 2,1,1,0 0,5,0,0 a 4 5,0,0,0 4,1,0,0 3,2,0,0 3,0,1,0 3,3,0,0 1,2,1,0 a 5 6,0,0,0 5,1,0,0 4,2,0,0 4,0,1,0 2,4,0,0 a 6 7,0,0,0 6,1,0,0 5,2,0,0 3,1,1,0 a 7 8,0,0,0 7,1,0,0 4,3,0,0 a 8 9,0,0,0 5,0,1,0 a 9 6,2,0,0 a 10 8,1,0,0 a 11 10,0,0,0 Fig. 3. Average cost per unit time of the policies determined by the algorithms (Problem 1).

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Estimation of Saturation Percentage of Soil Using Multiple Regression, ANN, and ANFIS Techniques

Cited by 27 publications

References 13 publications

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Investigations on factors influencing the CBR value of some Aegean sands

A reinforcement learning algorithm with fuzzy approximation for semi Markov decision problems

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