Support Vector Machine and regression analysis to predict the field hydraulic conductivity of sandy soil

Elbisy, Moussa S.

doi:10.1007/s12205-015-0210-x

Cited by 60 publications

(18 citation statements)

References 21 publications

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“…Observing some recent field-based studies, illustrated in Table 6, RF regression is recognized as superior modeling method in predicting the infiltration rate and hydraulic conductivity of soil in the region of Kurukshetra, India (Kumar & Sihag, 2019;Singh et al, 2017). Regarding the infiltration characteristics, the learning suitability of SVM and GP regression methods cannot be ignored as some of the studies suggest better accuracy in measuring the infiltration properties in contrast to some of the other popular soft computing approaches (Das et al, 2011;Elbisy, 2015;Sihag, Tiwari & Ranjan (2017b); Sihag et al, 2018a, 2018b; Vand et al, 2018;Sihag et al, 2018d;Singh et al, 2019a). Reviewing these data-mining-based studies; ANN, SVM, GP, and RF regression techniques come out to be strong modelling tools in determining the infiltration characteristics of soil in the field as well as in the laboratory (Table 6), so the authors acknowledged the combined utility of these modelling tools in an attempt to compare the prediction performance in simulating the infiltration rate through mixed soil of variable material of different characteristics (sand, rice husk ash, and fly ash) with basic soil properties.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Assessment of infiltration models developed using soft computing techniques

Sihag

Kumar

Singh³

2020

Geology, Ecology, and Landscapes

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“…Figure 2.2 shown nonlinear SVM. SVM can solve the classification problems and is also extended for regression tasks [17]. The term SVM is typically used to describe classification with support vector methods and support vector regression (SVR) being used to describe regression [18].…”

Section: Figure 22: Support Vector Regressionmentioning

confidence: 99%

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

Antony¹,

Prajna²,

Jnanesh³

2019

IJRTE

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In the present study, machine learning approaches have been developed to predict the 180 days aged core shear strength of sandwich composites. The characteristics of the sandwich composites depends on the number of factors namely fibre type i.e., Chopped strand Mat, Stitched, Chopped strand Mat and Woven Roving, core density, bond between the core and the face sheets and the ability to bear the load in flexural mode. In the current approach deep learning and SVR models were worked out by taking on six different parameters namely foam density, aging temperature and variety of fiber types as input variables. For each set of these input variables, the 180 days aged shear strength of sandwich composites with a test frequency of 30 days was determined. The paper aims at predicting the core shear strength value of stitch bond sandwich composites using other three aforementioned fibers. To create the model and confirm the accuracy of the algorithm training and test data are considered. The results obtained revealed that the deep learning model develo ped provides better predictive ability than the model of SVR.

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“…In recent years, machine learning algorithms have been widely employed for efficient simulations of high dimensional and nonlinear relationships of various hydrological variables in surface and subsurface hydrology. They have been employed to predict streamflow (Wu and Chau, 2010;Rasouli et al, 2012;Senthil Kumar et al, 2013;He et al, 2014;Shortridge et al, 2016;Abdollahi et al, 2017;Singh et al, 2018;Yuan et al, 2018;Adnan et al, 2019bAdnan et al, , 2021bDuan et al, 2020), groundwater and lake water level (Yoon et al, 2011;Tapoglou et al, 2014;Li et al, 2016;Sahoo et al, 2017;Sattari et al, 2018;Malekzadeh et al, 2019;Sahu et al, 2020;Yaseen et al, 2020;Kardan Moghaddam et al, 2021), water quality parameters such as nitrogen, phosphorus, and dissolved oxygen (Chen et al, 2010;Singh et al, 2011;Liu and Lu, 2014;Kisi and Parmar, 2016;Granata et al, 2017;Sajedi-Hosseini et al, 2018;Najah Ahmed et al, 2019;Knoll et al, 2020), soil hydraulic conductivity (Agyare et al, 2007;Das et al, 2012;Elbisy, 2015;Sihag, 2018;Araya and Ghezzehei, 2019;Adnan et al, 2021a), soil moisture (Gill et al, 2006;Ahmad et al, 2010;Coopersmith et al, 2014;…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

2021

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Estimating the maximum event water fraction, at which the event water contribution to streamflow reaches its peak value during a precipitation event, gives insight into runoff generation mechanisms and hydrological response characteristics of a catchment. Stable isotopes of water are ideal tracers for accurate estimation of maximum event water fractions using isotopic hydrograph separation techniques. However, sampling and measuring of stable isotopes of water is laborious, cost intensive, and often not conceivable under difficult spatiotemporal conditions. Therefore, there is a need for a proper predictive model to predict maximum event water fractions even at times when no direct sampling and measurements of stable isotopes of water are available. The behavior of maximum event water fraction at the event scale is highly dynamic and its relationships with the catchment drivers are complex and non-linear. In last two decades, machine learning algorithms have become increasingly popular in the various branches of hydrology due to their ability to represent complex and non-linear systems without any a priori assumption about the structure of the data and knowledge about the underlying physical processes. Despite advantages of machine learning, its potential in the field of isotope hydrology has rarely been investigated. Present study investigates the applicability of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict maximum event water fractions in streamflow using precipitation, soil moisture, and air temperature as a set of explanatory input features that are more straightforward and less expensive to measure compared to stable isotopes of water, in the Schwingbach Environmental Observatory (SEO), Germany. The influence of hyperparameter configurations on the model performance and the comparison of prediction performance between optimized ANN and optimized SVM are further investigated in this study. The performances of the models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). For the ANN, the results showed that an appropriate number of hidden nodes and a proper activation function enhanced the model performance, whereas changes of the learning rate did not have a major impact on the model performance. For the SVM, Polynomial kernel achieved the best performance, whereas Linear yielded the weakest performance among the kernel functions. The result showed that maximum event water fraction could be successfully predicted using only precipitation, soil moisture, and air temperature. The optimized ANN showed a satisfactory prediction performance with MAE of 10.27%, RMSE of 12.91%, R2 of 0.70, and NSE of 0.63. The optimized SVM was superior to that of ANN with MAE of 7.89%, RMSE of 9.43%, R2 of 0.83, and NSE of 0.78. SVM could better capture the dynamics of maximum event water fractions across the events and the predictions were generally closer to the corresponding observed values. ANN tended to underestimate the events with high maximum event water fractions and to overestimate the events with low maximum event water fractions. Machine learning can prove to be a promising approach to predict variables that are not always possible to be estimated due to the lack of routine measurements.

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Support Vector Machine and regression analysis to predict the field hydraulic conductivity of sandy soil

Cited by 60 publications

References 21 publications

Assessment of infiltration models developed using soft computing techniques

Assessment of infiltration models developed using soft computing techniques

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow

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