Parveen Sihag

Civil and Structural Engineering · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 6 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 39 citations into it; #3,628 of 6,160 by trust here, #3,123 by citations
• Environmental Engineering — 12 citations into it
• Artificial Intelligence — 8 citations into it
• Soil Science — 7 citations into it; #1,518 of 2,166 by trust here, #1,723 by citations

Whom Parveen Sihag trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Vladimir Naumovich Vapnik
2. Donald F. Specht
3. N. K. Tiwari
4. Isik Yilmaz
5. Ozgur Kisi
6. S. Ranjan
7. Simon S. Haykin
8. S. Mishra
9. Mesut Cimen
10. Vladimir Cherkassky

Who trusts Parveen Sihag overall

1. N. K. Tiwari
2. S. Ranjan

Papers from Parveen Sihag's perspective

Ordered by how strongly the researchers Parveen Sihag trusts endorse each paper. Same corpus, a different order for every researcher.

1. Comparison of infiltration models
2. Modelling infiltration and quantifying spatial soil variability in a wasteland of Kharagpur, India
3. Statistical learning theory
4. Using Support Vector Machines to Develop Pedotransfer Functions for Water Retention of Soils in Poland
5. The Nature Of Statistical Learning Theory
6. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils (Expert systems with applications)
7. A general regression neural network
8. Evaluation of some infiltration models and hydraulic parameters
9. A wavelet-support vector machine conjunction model for monthly streamflow forecasting
10. Neural Networks: A Comprehensive Foundation

Their papers

• Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques (2021) — 54 citations
• Soft computing techniques: Systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times (2021) — 94 citations
• Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data (2020) — 56 citations
• Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS) (2019) — 100 citations
• Comparison of infiltration models in NIT Kurukshetra campus (2018) — 48 citations
• Support vector regression-based modeling of cumulative infiltration of sandy soil (2018) — 61 citations
• Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network (2018) — 57 citations
• Comparative Evaluation of Infiltration Models (2018) — 73 citations
• Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression (2018) — 101 citations
• Estimation and inter-comparison of infiltration models (2017) — 95 citations