Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Sihag, Parveen; Tiwari, N. K.; Ranjan, Subodh

doi:10.1080/09715010.2017.1381861

Cited by 88 publications

(32 citation statements)

References 25 publications

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“…The performance of Polynomial kernel is found best with both SVM and GP regression, as compared to RBF kernel. So this study and some past studies (Kumar & Sihag, 2019;Sihag et al, 2019;Singh et al, 2017) affirm that RF model is a reasonably good predictor of infiltration characteristics in the field as well as in laboratory and can be successfully employed in both cases.…”

Section: Discussionsupporting

confidence: 75%

Assessment of infiltration models developed using soft computing techniques

Sihag

Kumar

Singh³

2020

Geology, Ecology, and Landscapes

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In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip's model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate.

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Section: Discussionsupporting

confidence: 75%

Assessment of infiltration models developed using soft computing techniques

Sihag

Kumar

Singh³

2020

Geology, Ecology, and Landscapes

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“…The study resulted in accurate predictions with Gaussian process regression (GPR) approach relative to other models. In a similar type of laboratory data, and Tiwari et al (2017) and Sihag et al (2019a) showed successful utilization of ANFIS in modelling the cumulative infiltration and the unsaturated hydraulic conductivity of soil samples. Some latest studies suggested successful application of soft computing techniques, viz.…”

Section: Introductionmentioning

confidence: 83%

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

2019

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Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (M5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (MNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R 2 , RMSE, MAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to M5 model tree and MNLR.

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“…However, the core samples and pumping test data obtained from field sampling are a bit limited to a certain extent. In recent decades, the efficient use of field and experimental data to invert the spatial distribution of hydraulic conductivity (K) has been widely studied by hydrogeologists [7,8]. At the end of the 20th century, Yeh et al [9] developed a hydraulic tomography (HT) method that was different from the traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Fine Characterization of the Effects of Aquifer Heterogeneity on Solute Transport: A Numerical Sandbox Experiment

Zhang

et al. 2019

Water

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Hydraulic conductivity (K) and the specific storage coefficient (S) are among the most important hydrogeological parameters of an aquifer. Traditionally, the hydrogeological parameters of a field aquifer system are mainly determined through a range of experiments that are both time-consuming and of poor operability. To accurately characterize aquifer heterogeneity, a synthetic sandbox is constructed using VSAFT2 (Variably Saturated Flow and Transport utilizing the Modified Method of Characteristics, in 2D) as a reference aquifer system by incorporating multilevel a priori geologic information into the sandbox configuration. The spatial distribution of the field of hydraulic conductivity (i.e., K) is inversely obtained by hydraulic tomography (HT). Then HT is compared with traditional kriging-estimated method in the fine characterization of aquifer heterogeneity, and the optimal K field is eventually selected to predict the solute transport. The influence of the number of pumping cycles on the accuracy of heterogeneity characterization is also discussed. The results show that the accuracy of the inversely obtained K field is improved with the increased number of pumping cycles. When incorporating multilevel a priori geological information, HT can characterize aquifer heterogeneity more finely than traditional kriging, and there is also a very good fitting of solute transport between the optimally estimated K field and the reference K field. Our study highlights the importance of the fine characterization of aquifer heterogeneity for the prediction of solute transport.

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Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Cited by 88 publications

References 25 publications

Assessment of infiltration models developed using soft computing techniques

Assessment of infiltration models developed using soft computing techniques

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

Fine Characterization of the Effects of Aquifer Heterogeneity on Solute Transport: A Numerical Sandbox Experiment

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