Parveen Sihag

Assistant Professor / Engineering & Technology
Assistant Professor

About

Parveen Sihag

Dr. Parveen Sihag holds a Ph.D.( Civil Engg. National Institute of Technology, Kurukshetera). He has completed his M.Tech (water resources Engineering) from NIT Kurukshetera, . He has teaching experience of 1 year. Area of Interest is hydrology, hydraulic structure, Air quality, soft computing Techniques and many more.

Publications

  • Alireza Sepah Vand, Parveen Sihag, Balraj Singh, Mehran Zand: Comparative Evaluation of Infiltration Models. KSCE Journal of Civil Engineering, Springer. 07/2018;, DOI:10.1007/s12205-018-1347-1
  • Mehdipour, V., Stevenson D.S., Memariianfard, M., and Sihag P., (2018) Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Quality Atmosphere & Health, Springer. DOI: 10.1007/s11869-018-0615-z
  • Kumar, M., and Sihag, P. (2019) Assessment of infiltration rate of soil using empirical and machine learning based models. Irrigation and Drainage (accepted).
  • Sihag, P., Esmaeilbeiki, F., Singh, B., Ebtehaj, I. and Bonakdari, H. (2019), Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques. Soft Computing, pp.1-14.
  • Tiwari, N. K., Sihag, P., Singh, B. K., Ranjan, S., & Singh, K. K. (2019). Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1-16.
  • Sihag, P., Singh, V.P., Anglaki, A., Kumar, V., Sepahvand, A., and  Golia E., (2019) Modelling of infiltration using artificial intelligence techniques in semi-arid Iran.Hydrological Sciences Journal. DOI 10.1080/02626667.2019.1659965
  • Sihag, P., Kumar, V., Afghan, F.R., Pandhiani, S.M. and Keshavarzi, A. (2019) Predictive modeling of PM2.5 using soft computing techniques: case study: Faridabad, Haryana, India. Air Quality Atmosphere & Health, Springer.
  • Pandhiani, S.M., Sihag, P., Singh, B., Shabri, A.B. and Pham, Q.B. (2019)  Time-series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques, Journal of irrigation and drainage, ASCE (accepted)
  • Sihag, P. Suthar, M. and Mohanty, S.(2019) Estimation of UCS-FT of Dispersive Soil Stabilized with Fly Ash, Cement Clinker and GGBS by Artificial Intelligence. Iranian Journal of Science and Technology, Transactions of Civil Engineering, DOI: 10.1007/s40996-019-00329-0.
  • Angelaki, A., Sihag, P., Sakellariou–Makrantonaki, M. and Tzimopoulos, C., 2020. The effect of sorptivity on cumulative infiltration. Water Supply.
  • Esmaeilbeiki, F., Nikpour, M.R., Singh, V.K., Kisi, O., Sihag, P. and Sanikhani, H., 2020. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables. Journal of Cleaner Production, 276, p.124206.
  • Mohammed, A., Rafiq, S., Sihag, P., Kurda, R. and Mahmood, W., 2020. Soft computing techniques: systematic multiscale models to predict the compressive strength of HVFA concrete based on mix proportions and curing times. Journal of Building Engineering, p.101851.
  • Mohammed, A., Rafiq, S., Sihag, P., Kurda, R., Mahmood, W., Ghafor, K. and Sarwar, W., 2020. ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash. Journal of Materials Research and Technology, 9(6), pp.12416-12427.
  • Mohammed, A., Rafiq, S., Sihag, P., Mahmood, W., Ghafor, K. and Sarwar, W., 2020. ANN, M5P-tree model, and nonlinear regression approaches to predict the compression strength of cement-based mortar modified by quicklime at various water/cement ratios and curing times. Arabian Journal of Geosciences, 13(22), pp.1-16.
  • Nouri, M., Sihag, P., Salmasi, F. and Abraham J. P. (2020).Prediction of homogeneous earthen slope safety factors using the forest and tree based modelling.Geotechnical and Geological Engineering
  • Nouri, M., Sihag, P., Salmasi, F. and Kisi, O., 2020. Energy loss in skimming flow over cascade spillways: Comparison of artificial intelligence-based and regression methods. Applied Sciences, 10(19), p.6903.
  • Salih, A., Rafiq, S., Sihag, P., Ghafor, K., Mahmood, W. and Sarwar, W., 2020. Systematic Multiscale Models to Predict the Effect of High-Volume Fly Ash on the Maximum Compression Stress of Cement-Based Mortar at Various Water/Cement Ratios and Curing Times. Measurement, p.108819.
  • Salmasi, F., Nouri, M., Sihag, P. and Abraham, J., 2020. Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data. Water Supply.
  • Sephavand A., Singh B.,  Ghobadi M., and Sihag P., (2020). Estimation of Infiltration Rate using Data-Driven Models. Arabian Journal of Geosciences
  • Yaseen, Z.M., Sihag, P., Yusuf, B. and Al‐Janabi, A.M.S., 2020. Modelling infiltration rates in permeable stormwater channels using soft computing techniques. Irrigation and Drainage.

Papers published in Scopus Journals:

  • Sihag P, Tiwari NK, Ranjan S. (2017) Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH Journal of Hydraulic Engineering, Taylor and Francis:1-11.https://doi.org/10.1080/ 09715010.2017.13818 61.
  • Sihag P, Tiwari NK, Ranjan S. (2018) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH Journal of Hydraulic Engineering, Taylor and Francis: 1-7. https://doi.org/10.1080/09715010.2018.1439776.
  • Sihag P, Tiwari NK & Ranjan S., (2018) Prediction of cumulative infiltration of sandy soil using random forest approach. Journal of Applied Water Engineering and Research, Taylor and Francis DOI: 10.1080/23249676.2018.1497557
  • Sihag, P., Singh, B., Sepah Vand, A., & Mehdipour, V. (2018). Modeling the infiltration process with soft computing techniques. ISH Journal of Hydraulic Engineering, Taylor and Francis, 1-15. DOI:10.1080/09715010.2018.1464408
  • Singh, B., Sihag, P., Singh, K., & Kumar, S. (2018). Estimation of trapping efficiency of vortex tube silt ejector. International Journal of River Basin Management, Taylor and Francis: 1-38. DOI:10.1080/15715124.2018.1476367
  • Tiwari, N. K., & Sihag, P. (2018). Prediction of oxygen transfer at modified Parshall flumes using regression models. ISH Journal of Hydraulic Engineering, Taylor and Francis, 1-12.
  • Tiwari, N.K., Sihag, P., Kumar, S. and Ranjan, S., 2018. Prediction of trapping efficiency of vortex tube ejector. ISH Journal of Hydraulic Engineering, Taylor and Francis, pp.1-9.
  • Angelaki, A., Nain, S.S., Singh, V. and Sihag, P., 2018. Estimation of models for cumulative infiltration of soil using machine learning methods. ISH Journal of Hydraulic Engineering, Taylor and Francis, pp.1-8.
  • Somvir Singh Nain, Parveen Sihag, Sunil Luthra: Performance Evaluation of Fuzzy-Logic and BP-ANN Methods for WEDM of Aeronautics Super Alloy. MethodsX, Elsevier.  04/2018; 5., DOI:10.1016/j.mex.2018.04.006
  • Singh, B., Shag, P., Pandhiani, S.M., Debnath, S. and Gautam, S. (2018). Estimation of permeability of soil using easily measured soil parameters: Assessing the artificial intelligence based models. ISH Journal of Hydraulic Engineering, Taylor and Francis (accepted)
  • Mohanty, S., Roy, N., Singh, S.P. and Sihag, P. (2019), Estimating the Strength of Stabilized Dispersive Soil with Cement Clinker and Fly Ash. Geotechnical and Geological Engineering, pp.1-12.
  • Sepahvand, A., Singh, B., Sihag, P., Nazari Samani, A., Ahmadi, H. and Fiz Nia, S., 2019. Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR). ISH Journal of Hydraulic Engineering, pp.1-12.
  • Somvir Singh Nain, Ravinder sai, Parveen Sihag, Sergij Vombol and Viola Vombol: Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. Archives of Materials Science and Engineering 1(95):12-19 DOI: 10.5604/01.3001.0013.1422
  • MOHANTY, S., Roy, N., Singh, S.P. and Sihag, P., 2019. Effect of industrial by-products on the strength of stabilized dispersive soil. International Journal of Geotechnical Engineering, pp.1-13.
  • Tiwari, N.K., Sihag, P. and Das, D., 2019. Performance evaluation of tunnel type sediment excluder efficiency by machine learning. ISH Journal of Hydraulic Engineering, pp.1-13.
  • Kharb, S. S., Antil, P., Singh, S., Antil, S. K., Sihag, P., & Kumar, A. Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites. Silicon (2020). https://doi.org/10.1007/s12633-020-00497-z
  • Sharma, N., Singh Thakur, M., Goel, P.L. and Sihag, P., 2020. A review: sustainable compressive strength properties of concrete mix with replacement by marble powder. Journal of Achievements in Materials and Manufacturing Engineering, 98(1).
  • Singh Nain, S., Sai, R., Sihag, P., Vambol, S. and Vambol, V., 2019. Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. Archives of Materials Science and Engineering, 95(1).
  • Kharb, S.S., Antil, P., Singh, S., Antil, S.K., Sihag, P. and Kumar, A., Machine Learning-Based Erosion Behavior of Silicon Carbide Reinforced Polymer Composites.
  • Sharma, N., Singh Thakur, M., Goel, P.L. and Sihag, P., 2020. A review: sustainable compressive strength properties of concrete mix with replacement by marble powder. Journal of Achievements in Materials and Manufacturing Engineering, 98(1).
  • Sihag P., Al- Janabi A. M. S., Alomari N. K., Ghani AB A. and Nain S.S. (2020). Evaluation of Tree Regression Analysis for Estimation of River Basin Discharge.Modeling Earth Systems and Environment, 
  • Sihag, P., Angelaki, A. and Chaplot, B., 2020. Estimation of the recharging rate of groundwater using random forest technique. Applied Water Science, 10(7), p.182.

Papers published in web of science  Journals:

  • Sihag P, Tiwari NK & Ranjan S. (2017) Modelling of infiltration of sandy soil using Gaussian process regression. Modelling Earth Systems and Environment, Springer 3(3): 1091-1100. https://doi.org/10.1007/s40808-017-0357-1.
  • Singh, B., Sihag, P., & Singh, K. (2017). Modelling of impact of water quality on  infiltration rate of soil by random forest regression. Modeling Earth Systems and Environment, Springer 3(3), 999-1004.
  • Sihag P, Tiwari NK, & Ranjan S. (2017) Estimation and inter-comparison of infiltration models. Water Science, Elsevier, 31(1): 34-43. https://doi.org/10.1016/j.wsj.2017.03 .001.
  • Singh, B., Sihag, P. & Singh, K., (2018). Comparison of infiltration models in NIT Kurukshetra campus. Applied Water Science, Springer 8(2), p.63.
  • Sihag, P., Jain, P., & Kumar, M., (2018). Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression. Modeling earth systems and environment, Springer 4(1), pp.61-68.
  • Sihag, P., (2018). Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, Springer 4(1), pp.189-198.
  • Sihag, P., Singh, B., Gautam, S., & Debnath, S., (2018). Evaluation of impact of fly ash on infiltration characteristics using different soft computing techniques. Applied Water Science, Springer 8(2), p.187.
  • Sihag, P., Kumar, M. and Singh, V., (2018) Enhanced soft computing for ensemble approach to estimate the compressive strength of high strength concrete. Journal of Materials and Engineering Structures «JMES», 6(1), pp.93-103.
  • Sihag, P., Esmaeilbeiki, F., Singh, B. and Pandhiani, S.M., 2019. Model-based soil temperature estimation using climatic parameters: the case of Azerbaijan Province, Iran. Geology, Ecology, and Landscapes, Taylor and Francis pp.1-13.
  • Sihag, P., Keshavarzi, A. and Kumar, V., 2019. Comparison of different approaches for modeling of heavy metal estimations. SN Applied Sciences, 1(7), p.780.
  • Sihag, P., Kumar, M. and Singh B. (2019) Assessment of Infiltration Models Developed Using Soft Computing Techniques. Geology, Ecology, and Landscapes, Taylor and Francis pp.1-13.
  • Kumar, M. Sihag, P. Tiwari NK, & Ranjan S. (2019). Experimental study and Modelling discharge coefficient of trapezoidal and rectangular piano key weirs. Applied Water Science, Springer 8(2), p.63.
  • Sihag, P., Angelaki, A. and Chaplot, B., 2020. Estimation of the recharging rate of groundwater using random forest technique. Applied Water Science, 10(7), pp.1-11.
  • Singh, B., Sihag, P., Parsaie, A. and Angelaki, A., 2020. Comparative analysis of artificial intelligence techniques for the prediction of infiltration process. Geology, Ecology, and Landscapes, pp.1-10.

Papers accepted or published in Indian Journals:

  • Tiwari NK, Sihag P., & Ranjan S. (2017). Modeling of Infiltration of soil using Adaptive Neuro-fuzzy Inference System (ANFIS). Journal of Engineering & Technology Education, 11 (1):  13-21.
  • Sihag, P., Tiwari, NK., & Ranjan S. (2017). Modelling of Unsaturated Hydraulic Conductivity of soil. Indian Water Resources Society37(4).
  • Sihag P, Tiwari NK, & Ranjan, S. (2017). Performance Evaluation of Infiltration models. Indian Water Resources Society (1461). Accepted December, 2017.
  • Sihag, P., & Singh, B. (2018). Field evaluation of infiltration models. 10.5281/zenodo. 1239447.
  • Sihag P, Pandhiani, S.M., Singh, V., & Debnath, S. (2018). Prediction of permeability of soil using Random tree. Indian Water Resources Society (1488). Accepted september, 2018.
  • Singh, B., Singh, K. Kumar, R. and Sihag, P., Future Prediction and Trend Analysis ofTemperature of Haryana. Indian Water Resources Society (1462). Accepted June, 2018.
  • Sihag, P., Singh, B., Jain, P. and Singh V., (2018) Estimation of Impact of Impurities on Recharging Rate of Medium-Sand Filter System.Indian Water Resources Society (1470). Accepted, November 2017.
  • Gautam, S., Sihag, P., Tiwari, N.K. and Ranjan, S., 2019. Neuro-Fuzzy Approach for Predicting the Infiltration of Soil. In Environmental Geotechnology (pp. 221-228). Springer, Singapore.

List of Publications in Conferences

  • Rahul Agarwal, Parveen Sihag & N K Tiwari (2015): The Study of Infiltration Characteristics of Locally Soil (N.I.T Kurukshetra Campus).  JNU, New Delhi, India.
  • Sihag P, Tiwari NK, & Ranjan, S. (2015). Investigation of infiltration characteristics. Symposium on hydrology, New Delhi India.
  • Karan Singh, Parveen Sihag & Ritesh Kumar (2014): Downscaling Monthly Rainfall Using Support Vector Regression and M5P Model Tree. 5th international conference on “Architecture, Civil and Environmental Engineering” (ACEE-2014).
  • Balraj Singh, Parveen Sihag & Diwan Singh (2014): Study of Infiltration Characteristics of Locally Soils. 5th international conference on “Architecture, Civil and Environmental Engineering” (ACEE-2014).
  • Sihag P, Tiwari NK, & Ranjan, S. (2013). Study of Infiltration characteristics of soils. (CAHHEWC-2013), National Institute of Technology, Kurukshetra, India. 
  • Sihag P and Nain S.S. (2018). Modeling of infiltration rate using data mining models. International Conference on Civil engineering practices and trends, 21st - 22nd December 2018 in CMR College of Engineering and Technology, Hyderabad.

Book chapters

  • Book Chapters in Pharmaceutical waste water treatment technologies, ,Concept and implementation strategies in IWA publications.
  • PavloKozub, Svetlana Kozub , NiloofarMozaffari  , ParveenSihag , NastaranMozaffari , SergijVambol ,Viola Vambol  , NishanaRamsawak  (2020).Treatment Schemes- conventional and dedicated for PhACs Treatment
  • Viola Vambol  , AndriiShulha , SergijVambol  , ParveenSihag  , VitaliiPavlykivskyi  , NiloofarMozaffari  ,NastaranMozaffari  , Nadeem A Khan , Barbara Sawicka (2020) Legislative and criminal law aspects of water protection and prevention of pharmaceuticals accumulation in nature

 

 

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