Parveen Sihag

Bio: Parveen Sihag is an academic researcher from National Institute of Technology, Kurukshetra. The author has contributed to research in topics: Compressive strength & Infiltration (hydrology). The author has an hindex of 19, co-authored 77 publications receiving 904 citations.

Modelling of impact of water quality on infiltration rate of soil by random forest regression

Abstract: In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. A dataset consists of 132 field measurements were used. Out of 132 observations randomly selected 88 observations were used for training, whereas remaining 44 were used for testing the model. Input variables consist of cumulative time (Tf), type of impurities (It), concentration of impurities (Ci), and moisture content (Wc) whereas the infiltration rate was considered as output. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE) and root relative square error (RRSE) were considered to compare the performance the both modelling approaches. The result of evolution suggests that Random forest regression approach works well than the other two models (ANN and M5P model tree). The estimated value of infiltration rate using Random forest regression lies within ±25% error lines. Sensitivity analysis suggests that cumulative time is an important parameter for predicting the infiltration rate of the soil.

Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression

Abstract: In this paper, recharging rate of stormwater filter system is assessed by using predictive models of Gaussian Process (GP) and Support Vector Machines (SVM). Four kernel functions: normalized poly kernel, polynomial kernel, Pearson VII kernel (PUK) and radial basis kernel (RBF) were used with both modelling approaches (GP and SVM). A dataset consists of 678 measurements was collected from the experimental investigations on the infiltration of the storm-water filter system. Out of 678 observations, randomly selected 462 observations were used for training, whereas remaining 216 were used for testing the model. Input variables were comprise of cumulative time (T), the thickness of medium sand bed (B), size of medium sand (S) and concentration of impurities (Conc.), whereas the recharging rate (R) was considered as output. Correlation coefficient (C.C) and root mean square error (RMSE) were used to compare the performance of both modelling approaches. The evaluation of result suggests that Pearson VII based GP regression approach works well as compared to the other kernel functions based on GP and SVM models. Sensitivity analysis suggested that the size of medium sand (S) is an important parameter for predicting the recharging rate of stormwater filter system. Parametric study outcomes suggested that the higher size of medium sand increases the recharging rate, and a higher concentration of impurities in water decreases the recharging rate. Moreover, on expanding the thickness of the medium sand bed the recharging rate of the storm water filter system was observed to be increased.

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

Abstract: This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

Modelling of infiltration of sandy soil using gaussian process regression

Abstract: The aim of this paper to assesses the potential of machine learning approaches, i.e. multi-linear regression (MLR), support vector regression (SVR), Gaussian process (GP) regression of cumulative infiltration and compares their performances with three traditional models [Kostiakov model, US-Soil Conservation Service (SCS) model and Philip’s model]. Data set as many as 413 were obtained by conducting experiments in laboratory of NIT Kurukshetra. It is observed from the experiments that moisture content influences the cumulative infiltration of soil. Out of 413 data set 289 arbitrary selected were used for training the models, whereas remaining 124 were used for testing. Input data set consist of time, sand, rice husk ash, fly ash, suction head, bulk density and moisture content where as cumulative infiltration was considered as output. Two kernel function i.e. Pearson VII and radial based kernel function were used with both SVR and GP regression. The results after comparison suggests that the GP regression based approach works better than SVR, MLR, Kostiakov model, SCS model and Philip’s model approaches and it could be successfully used in prediction of cumulative infiltration data.

Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times

Abstract: Advances in technology and environmental issues allow the building industry to use ever more high-performance engineered materials. In this study, the hardness of concrete mixtures with high-volume fly ash (HVFA) has been evaluated and modeled for the LEED (Leadership for Energy and Environmental Design). High-performance building materials may have greater strength, ductility, external factor resistance, more environmentally sustainable construction, and lower cost than conventional building materials. To overcome the mentioned matter, this study aims to establish systematic multiscale models to predict the compressive strength of concrete mixes containing a high volume of fly ash (HVFA) and to be used by the construction industry with no theoretical restrictions. For that purpose, a wide experimental data (a total of 450 tested HVFA concrete mixes) from different academic research studies have been statically analyzed and modeled. For that purpose, Linear, Nonlinear Regressions, Multi-logistic Regression, M5P-tree, and Artificial Neural Network (ANN) technical approaches were used for the qualifications. In the modeling process, most relevant parameters affecting the strength of concrete, i.e. fly ash (class C and F) incorporation ratio (0–80% of cement's mass), water-to-binder ratio (0.27–0.58), and gravel, sand, cement contents and curing ages (3–365 days). According to the correlation coefficient (R) and the root mean square error, the compressive strength of HVFA concrete can be well predicted in terms of w/b, fly ash, cement, sand, and gravel densities, and curing time using various simulation techniques. Among the used approaches and based on the training data set, the model made based on the ANN, M5P-tree, and Non-linear regression models seem to be the most reliable models. The results of this study suggest that the M5Ptree-based model is performing better than other applied models using training and testing datasets. The maximum and minimum percentage of error between the actual test results and the outcome of the prediction using MLR, LR, M5P-tree, and ANN were 0.03–43%, 0.03–54%, 0.04–33%, and 0.03–41% respectively. Based on the outcomes from the models and statistical assessments such as coefficient of determination (R2), mean absolute error (MAE) and the root mean square error (RMSE), the models M5P-tree, ANN, and MLR respectively were predicted the compressive strength of the HVFA concrete very well with a high value of R2 and low values of MAE and RMSE based on the comparison with experimental data. The sensitivity investigation concludes that the curing time is the most dominating parameter for the prediction of the compressive strength of HVFA concrete with this data set.

An essay towards solving a problem in the doctrine of chances. [Facsimil]

Abstract: Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Abstract: Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Support vector machines: Theory and applications

Abstract: This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.