Hydraulic structure, Jet Aeration Systems, Application of Soft Computing, Artificial Intelligence and Machine Learning Techniques in Water Resources Systems, Experimental Hydraulics, Groundwater, Modeling,  Recharge of Groundwater, liquefication, Stabilization, Optimization and relevant applications in Civil Engineering.

Research Publications for the last 10-12 years:

Journals (National & International) – 47

Conferences (National & International) – 200

Book chapter – 07

http://www.scopus.com/inward/authorDetails.url?authorID=38962189900&partnerID=MN8TOARS

https://orcid.org/0000-0002-3153-2531

Ph.D Supervision

Completed: 05

Under progress: 03

M. Tech Supervision (from 2013 to present)

Completed: 45

Journal Papers published in the last 10-12 years:

1. Kumar, S., Ojha, C. S. P., Tiwari, N. K., & Ranjan, S. (2023). Exploring the potential of artificial intelligence techniques in the prediction of the removal efficiency of vortex tube silt ejector. International Journal of Sediment Research, 38(4), 615-627.
2. Luxmi, K. M., Tiwari, N. K., & Ranjan, S. (2023). Application of soft computing approaches to predict gabion weir oxygen aeration efficiency. ISH Journal of Hydraulic Engineering, 29(3), 244-258.
3. Luxmi, K. M., Tiwari, N. K., & Ranjan, S. (2023). Estimation and comparison of gabion weir oxygen mass transfer by ensemble learnings of bagging, boosting, and stacking algorithms. ISH Journal of Hydraulic Engineering, 29(sup1), 196-211.
4. Tiwari, A., Ojha, C. S. P., Tiwari, N. K., & Ranjan, S. (2023). Montana flume aeration performance evaluation with machine learning models. Journal of The Institution of Engineers (India): Series A, 104(1), 175-186.
5. Tiwari, N. K., Luxmi, K. M., & Ranjan, S. (2023). Predictive models for prediction of broad crested gabion weir aeration performance. Journal of Soft Computing in Civil Engineering, 7(2), 43-73.
6. Tiwari, N. K., Luxmi, K. M., & Ranjan, S. (2023). Estimating gabion weir oxygen transfer with data mining. Water Quality Research Journal, 58(1), 22-40.
7.  Kumar, S., Tiwari, N. K., & Ranjan, S. (2022). Applicability of several machine learning models in estimation of vortex tube trapping efficiency. Water Supply, 22(11), 8173-8195.
8.  Kumar, M., Tiwari, N. K., & Ranjan, S. (2022), Application of machine learning methods in estimating the oxygenation performance of various configurations of plunging hollow jet aerators. Journal of Environmental Engineering, 148(11), 04022070 (ASCE).
9. Kumar, M., Tiwari, N. K., & Ranjan, S. (2022). Soft computing-based predictive modelling of oxygen transfer performance of plunging hollow jets. ISH Journal of Hydraulic Engineering, 28(sup1), 223-233.
10. Tiwari, N. K., Sihag, P., & Das, D. (2022). Performance evaluation of tunnel-type sediment excluder efficiency by machine learning. ISH Journal of Hydraulic Engineering, 28(sup1), 27-39.
11. Kumar, S., Tiwari, N. K., & Ranjan, S. (2022). Applicability of several machine learning models in estimation of vortex tube trapping efficiency. Water Supply, 22(11), 8173-8195.
12. Verma, A., Ranjan, S., Ghanekar, U., & Tiwari, N. K. (2022). Soft computing techniques for predicting aeration efficiency of gabion stepped weir.) Optimization in Industrial and Manufacturing Systems and Applications(pp. 117-122). Springer International Publishing.
13. Srinivas, R., & Tiwari, N. K. (2022). Modelling of the oxygen aeration performance efficiency of gabion spillways. Water Practice & Technology, 17(11), 2317-2333.
14. Srinivas, R., & Tiwari, N. K. (2022). Oxygen aeration efficiency of gabion spillway by soft computing models. Water Quality Research Journal, 57(3), 215-232.
15. Tiwari, N. K. (2021). Evaluating hydraulic jump oxygen aeration by experimental observations and data-driven techniques. ISH Journal of Hydraulic Engineering, 27(sup1), 601-615.
16. Rakesh, G., & Tiwari, N. K. (2022). Assessment of groundwater at Kurukshetra district. Water Practice & Technology, 17(11), 2225-2241.
17.   Kumar, M., Tiwari, N. K., & Ranjan, S. (2021). Oxygenation by plunging jet aerators: A review. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 45(3), 1329-1348.
18. Kumar, M., Tiwari, N. K., & Ranjan, S. (2021). Experimental study on oxygen mass transfer characteristics by plunging hollow jets. Arabian Journal for Science and Engineering, 46, 4521-4532.
19. Sharafati, A., Haghbin, M., Tiwari, N. K., Bhagat, S. K., Al-Ansari, N., Chau, K. W., & Yaseen, Z. M. (2021). Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics, 15(1), 627-643.
20. Bodana, D., Tiwari, N. K., Ranjan, S., & Ghanekar, U. (2020). Estimation of the depth of penetration in a plunging hollow jet using artificial intelligence techniques. Archives of Materials Science and Engineering, 103(2).
21. Kumar, M., Sihag, P., Tiwari, N. K., & Ranjan, S. (2020). Experimental study and modelling discharge coefficient of trapezoidal and rectangular piano key weirs. Applied Water Science, 10(1), 43.
22. Kumar, M., Tiwari, N. K., & Ranjan, S. (2020). Prediction of oxygen mass transfer of plunging hollow jets using regression models. ISH Journal of Hydraulic Engineering, 26(1), 23-30.
23. Sihag, P., Tiwari, N. K., & Ranjan, S. (2020). Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH journal of hydraulic engineering, 26(1), 44-50.
24. Sharafati, A., Haghbin, M., Haji Seyed Asadollah, S. B., Tiwari, N. K., Al-Ansari, N., & Yaseen, Z. M. (2020). Scouring depth assessment downstream of weirs using hybrid intelligence models. Applied Sciences, 10(11), 3714.
25. Tiwari, N. K., & Sihag, P. (2020). Prediction of oxygen transfer at modified Parshall flumes using regression models. ISH Journal of Hydraulic Engineering, 26(2), 209-220.
26. Tiwari, N. K., Sihag, P., Kumar, S., & Ranjan, S. (2020). Prediction of trapping efficiency of vortex tube ejector. ISH Journal of Hydraulic Engineering, 26(1), 59-67.
27. Tiwari, N. K., Sihag, P., Singh, B. K., Ranjan, S., & Singh, K. K. (2020). Estimation of tunnel desilter sediment removal efficiency by ANFIS. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 44, 959-974.
28. Gautam, S., Sihag, P., Tiwari, N. K., & Ranjan, S. (2019). Neuro-fuzzy approach for predicting the Infiltration of soil. In Environmental Geotechnology: Proceedings of EGRWSE 2018(pp. 221-228). Springer Singapore.
29. Kumar, M., Tiwari, N. K., & Ranjan, S. (2019). Kernel function-based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator. Journal of Achievements in Materials and Manufacturing Engineering, 95(2).
30. Sihag, P., Tiwari, N. K., & Ranjan, S. (2019). Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH Journal of Hydraulic Engineering, 25(2), 132-142.
31. Sihag, P., Tiwari, N. K., & Ranjan, S. (2019). Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH Journal of Hydraulic Engineering, 25(2), 132-142.
32. Kumar, M., Ranjan, S., & Tiwari, N. K. (2018). Oxygen transfer study and modelling of plunging hollow jets. Applied Water Science, 8, 1-15.
33. Kumar, M., Ranjan, S., Tiwari, N. K., & Gupta, R. (2018). Plunging hollow jet aerators-oxygen transfer and modelling. ISH Journal of Hydraulic Engineering, 24(1), 61-67.
34. Sihag, P., Tiwari, N. K., & Ranjan, S. (2018). Prediction of cumulative infiltration of sandy soil using random forest approach. Journal of Applied Water Engineering and Research.
35. Tiwari, N. K., Sihag, P., & Ranjan, S. (2017). Modelling of infiltration of soil using adaptive neuro-fuzzy inference system (ANFIS). Journal of Engineering & Technology Education, 11(1), 13-21.
36. Wasi, M., Ranjan, S., Mahesh, A., & Tiwari, N. K (2017). AN EXPERT SYSTEM FOR PREDICTING AERATION PERFORMANCE OF WEIRS BY USING ANN AND RANDOM FOREST.
37. Sihag, P., Tiwari, N. K., & Ranjan, S. (2017). Estimation and inter-comparison of infiltration models. Water Science, 31(1), 34-43.
38. Sihag, P., Tiwari, N. K., & Ranjan, S. (2017). Modelling of infiltration of sandy soil using Gaussian process regression. Modeling Earth Systems and Environment, 3, 1091-1100.
39. Singh, B. K., Tiwari, N. K., & Singh, K. K. (2016). Support vector regression-based modelling of trapping efficiency of silt ejector. J Indian Water Resour Soc, 36(1), 41-49.
40. Singh B K, Singh KK & Tiwari NK (2016). Performance of Sediment Ejector, Hydrology Journal, Vol. 38 & 39, Issue 1- 4, pp. 42-58.
41. Sant, A. S., Pandey, R. P., & Tiwari, N. K. (2015). Drought analysis and supplemental irrigation requirement for rabi crops in Haryana state.
42. Pal, M., Singh, N. K., & Tiwari, N. K. (2014). Kernel methods for pier scour modelling using field data. Journal of Hydroinformatics, 16(4), 784-796.
43. Banik, P., Tiwari, N. K., & Ranjan, S. (2014). Comparative crop water assessment using meteorological data and modeling techniques. Global sustainability transitions: impacts and innovations, 168-180.
44. Pal, M., Singh, N. K., & Tiwari, N. K. (2013). Pier scour modelling using random forest regression. ISH Journal of Hydraulic Engineering, 19(2), 69-75.
45. Pal, M., Singh, N. K., & Tiwari, N. K. (2012). M5 model tree for pier scour prediction using field dataset. KSCE Journal of Civil Engineering, 16, 1079-1084.
46. Pal, M., Singh, N. K., & Tiwari, N. K. (2012). M5 model tree for pier scour prediction using field dataset. KSCE Journal of Civil Engineering, 16, 1079-1084.
47. al, M., Singh, N. K., & Tiwari, N. K. (2011). Support vector regression-based modelling of pier scour using field data. Engineering Applications of Artificial Intelligence, 24(5), 911-916.

Book Chapter -7

1. Mishra, S., Vajesnayee, S. R., & Tiwari, N. K. (2022, July). Technique of Utilization of Coal Waste in an Efficient and Effective Way. In International Conference on Advances in Energy Research(pp. 729-736). Singapore: Springer Nature Singapore.
2. Sood, S., Kumar, R., & Tiwari, N. K. (2022, July). Life Cycle Assessment of a Hybrid Solar Based Electric Vehicle Charging Station Using SimaPro. In International Conference on Advances in Energy Research(pp. 89-97). Singapore: Springer Nature Singapore.
3. Luxmi, K. M., Tiwari, A., Tiwari, N. K., & Vajesnayee, S. R. (2021, August). Development and evaluation of soft computing models for Montana flume aeration. In International Conference on Chemical, Bio and Environmental Engineering(pp. 167-180). Cham: Springer International Publishing.
4. Saran, D., & Tiwari, N. K. (2020, October). Generation of a Versatile Discharge Formula for Multiple Parshall Flumes Using a Regression Technique. In International Conference on Energy, Materials Sciences & Mechanical Engineering(pp. 1197-1207). Singapore: Springer Nature Singapore.
5. Verma, A., Ranjan, S., Ghanekar, U., & Tiwari, N. K. (2022). Soft computing techniques for predicting aeration efficiency of gabion stepped weir. In Proceedings of the International Conference on Industrial and Manufacturing Systems (CIMS-2020) Optimization in Industrial and Manufacturing Systems and Applications(pp. 117-122). Springer International Publishing.
6. Sangeeta, Ranjan, S., & Tiwari, N. K. (2019). Aeration Efficiency Evaluation of Modified Small Parshall Flume Using M5P and Adaptive Neuro-Fuzzy Inference System. In Sustainable Engineering: Proceedings of EGRWSE 2018(pp. 243-252). Springer Singapore.
7. Gautam, S., Sihag, P., Tiwari, N. K., & Ranjan, S. (2019). Neuro-fuzzy approach for predicting the Infiltration of soil. In Environmental Geotechnology: Proceedings of EGRWSE 2018(pp. 221-228). Springer Singapore.

Conferences for the last 10-12 years :

Experience :
22 years

Awards/Honours:

District scholarship up to 10th class

National Scholarship in Inter and Graduation
GATE Scholarship in M.Tech

Membership of Professional Societies:

Life member, Indian Society for Technical Education
Life member, Journal of Indian Water Resources Society
Life member, The Indian Society for Hydraulics

Short Term Courses & Conferences organised:

STC on MATLAB Programming for Engineers (Dec.11-15, 2013)
Conference, CAHHEWC-2013 (Nov.15-16, 2013), NIT Kurukshetra