Publications 

Peer-Reviewed Journal Publications 

Full List can be obtained from the Google Scholar. 

2008-2012

1.      Sivapragasam, C., Maheswaran, R., and Veena, V. (2008) ANN based Model for Aiding leak Detection in water Distribution Networks. Asian Journal of Water Environment and Pollution, Vol.5, No.3.

2.      Sivapragasam, C., Maheswaran, R., Venkatesh, V., 2010. Reply to comment on “Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological processes 22: 623-628.” Hydrol. Process. 24, 800. https://doi.org/10.1002/hyp.7512.

3.      Sivapragasam, C., Maheswaran, R., Venkatesh, V., 2008. Genetic programming approach for flood routing in natural channels. Hydrol. Process. 22, 623–628. https://doi.org/10.1002/hyp.6628

2012-2015

4.      Maheswaran, R., Khosa, R., 2015. Wavelet Volterra Coupled Models for forecasting of nonlinear and non-stationary time series. Neurocomputing 149, 1074–1084. https://doi.org/10.1016/j.neucom.2014.07.027

5.      Sudheer, C., Maheswaran, R., Panigrahi, B.K., Mathur, S., 2014. A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Comput. Appl. 24, 1381–1389. https://doi.org/10.1007/s00521-013-1341-y

6.      Maheswaran, R., Khosa, R., 2014. A Wavelet-Based Second Order Nonlinear Model for Forecasting Monthly Rainfall. Water Resour. Manag. 28, 5411–5431. https://doi.org/10.1007/s11269-014-0809-6

7.      Rathinasamy, M., Adamowski, J., Khosa, R., 2013. Multiscale streamflow forecasting using a new Bayesian Model Average based ensemble multi-wavelet Volterra nonlinear method. J. Hydrol. 507, 186–200. https://doi.org/10.1016/j.jhydrol.2013.09.025

8.      Maheswaran, R., Khosa, R., 2013a. Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River. J. Hydroinformatics 15, 1022–1041. https://doi.org/10.2166/hydro.2013.135

9.      Maheswaran, R., Khosa, R., 2013b. Long term forecasting of groundwater levels with evidence of non-stationary and nonlinear characteristics. Comput. Geosci. 52, 422–436. https://doi.org/10.1016/j.cageo.2012.09.030

10.  Maheswaran, R., Khosa, R., 2012a. Wavelet-Volterra coupled model for monthly stream flow forecasting. J. Hydrol. 450–451, 320–335. https://doi.org/10.1016/j.jhydrol.2012.04.017

11.  Maheswaran, R., Khosa, R., 2012b. Comparative study of different wavelets for hydrologic forecasting. Comput. Geosci. 46, 284–295. https://doi.org/10.1016/j.cageo.2011.12.015

12.  Rathinasamy, M., Khosa, R., 2012. Multiscale nonlinear model for monthly streamflow forecasting: A wavelet-based approach. J. Hydroinformatics 14, 424–442. https://doi.org/10.2166/hydro.2011.130

13.  Rathinasamy, M., Khosa, R., Adamowski, J., Ch, S., Partheepan, G., Anand, J. and Narsimlu, B., 2014. Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models. Water Resources Research, 50(12), pp.9721-9737.

2016-2020

14.  Bairwa, A.K., Khosa, R., Maheswaran, R., 2016. Developing intensity duration frequency curves based on scaling theory using linear probability weighted moments: A case study from India. J. Hydrol. 542, 850–859. https://doi.org/10.1016/j.jhydrol.2016.09.056

15.  Maheswaran, R., Khosa, R., Gosain, A.K., Lahari, S., Sinha, S.K., Chahar, B.R., Dhanya, C.T., 2016. Regional scale groundwater modelling study for Ganga River basin. J. Hydrol. 541, 727–741. https://doi.org/10.1016/j.jhydrol.2016.07.029

16.  Sehgal, V., Lakhanpal, A., Maheswaran, R., Khosa, R., Sridhar, V., 2018. Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling. J. Hydrol. 556, 1078–1095. https://doi.org/10.1016/j.jhydrol.2016.10.048

17.  Lakhanpal, A., Sehgal, V., Maheswaran, R., Khosa, R., Sridhar, V., 2017. A non-linear and non-stationary perspective for downscaling mean monthly temperature: a wavelet coupled second order Volterra model. Stoch. Environ. Res. Risk Assess. 31, 2159–2181. https://doi.org/10.1007/s00477-017-1444-6

18.  Rathinasamy, M., Bindhu, V.M., Adamowski, J., Narasimhan, B., Khosa, R., 2017. Investigation of the scaling characteristics of LANDSAT temperature and vegetation data: a wavelet-based approach. Int. J. Biometeorol. 61, 1709–1721. https://doi.org/10.1007/s00484-017-1353-x

19.  Agarwal, A., Maheswaran, R., Kurths, J., Khosa, R., 2016a. Wavelet Spectrum and Self-Organizing Maps-Based Approach for Hydrologic Regionalization -a Case Study in the Western United States. Water Resour. Manag. 30, 4399–4413. https://doi.org/10.1007/s11269-016-1428-1

20.  Agarwal, A., Maheswaran, R., Sehgal, V., Khosa, R., Sivakumar, B., Bernhofer, C., 2016b. Hydrologic regionalization using wavelet-based multiscale entropy method. J. Hydrol. 538, 22–32. https://doi.org/10.1016/j.jhydrol.2016.03.023

21.  Quilty, J., Adamowski, J., Khalil, B. and Rathinasamy, M., 2016. Bootstrap rank‐ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling. Water Resources Research, 52(3), pp.2299-2326.

22.  Agarwal, A., Marwan, N., Maheswaran, R., Ozturk, U., Kurths, J., Merz, B., 2020. Optimal design of hydrometric station networks based on complex network analysis. Hydrol. Earth Syst. Sci. 24, 2235–2251. https://doi.org/10.5194/hess-24-2235-2020

23.  Rathinasamy, M., Agarwal, A., Sivakumar, B., Marwan, N., Kurths, J., 2019. Wavelet analysis of precipitation extremes over India and teleconnections to climate indices. Stoch. Environ. Res. Risk Assess. 33, 2053–2069. https://doi.org/10.1007/s00477-019-01738-3

24.  Kurths, J., Agarwal, A., Shukla, R., Marwan, N., Rathinasamy, M., Caesar, L., Krishnan, R., Merz, B., 2019. Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach. Nonlinear Process. Geophys. 26, 251–266. https://doi.org/10.5194/npg-26-251-2019

25.  Agarwal, A., Caesar, L., Marwan, N., Maheswaran, R., Merz, B., Kurths, J., 2019. Network-based identification and characterization of teleconnections on different scales. Sci. Rep. 9, 1–12. https://doi.org/10.1038/s41598-019-45423-5

26.  Agarwal, Ankit, Maheswaran, R., Marwan, N., Caesar, L., Kurths, J., 2018. Wavelet-based multiscale similarity measure for complex networks. Eur. Phys. J. B 91. https://doi.org/10.1140/epjb/e2018-90460-6

27.  Agarwal, A., Marwan, N., Maheswaran, R., Merz, B., Kurths, J., 2018. Quantifying the roles of single stations within homogeneous regions using complex network analysis. J. Hydrol. 563, 802–810.

28.  Agarwal, A., Marwan, N., Rathinasamy, M., Merz, B., Kurths, J., 2017. Multi-scale event synchronization analysis for unravelling climate processes: A wavelet-based approach. Nonlinear Process. Geophys. 24, 599–611. https://doi.org/10.5194/npg-24-599-2017

29.  Yeditha, P.K., Rathinasamy, M., Agarwal, A.,SIvakumar B, (2020) Intercomparison of downscaling models with a special emphasis on wavelet based hybrid models. Journal of Hydrology, 126373

30.  Yeditha, P.K., Kasi, V., Rathinasamy, M., Agarwal, A., 2020. Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods. Chaos 30, 063115. https://doi.org/10.1063/5.0008195

31.  Guntu, R.K., Maheswaran, R., Agarwal, A., Singh, V.P., 2020. Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory. J. Hydrol. 590, 125236. https://doi.org/10.1016/j.jhydrol.2020.125236

32.  Guntu, R.K., Rathinasamy, M., Agarwal, A., Sivakumar, B., 2020. Spatiotemporal variability of Indian rainfall using multiscale entropy. J. Hydrol. 587, 124916. https://doi.org/10.1016/j.jhydrol.2020.124916

33.   Guntu, R.K., Yeditha, P.K., Rathinasamy, M., Perc, M., Marwan, N., Kurths, J., Agarwal, A., 2020. Wavelet entropy-based evaluation of intrinsic predictability of time series. Chaos 30. https://doi.org/10.1063/1.5145005

34.  Kasi, V., Pinninti, R., Landa, S.R., Rathinasamy, M., Sangamreddi, C., Kuppili, R.R., Dandu Radha, P.R., 2020. Comparison of different digital elevation models for drainage morphometric parameters: a case study from South India. Arab. J. Geosci. 13. https://doi.org/10.1007/s12517-020-06049-4

35.  Kasi, V., Yeditha, P.K., Rathinasamy, M., Pinninti, R., Landa, S.R., Sangamreddi, C., Agarwal, A., Dandu Radha, P.R., 2020. A novel method to improve vertical accuracy of CARTOSAT DEM using machine learning models. Earth Sci. Informatics. https://doi.org/10.1007/s12145-020-00494-1

36.  Setti, S.; Maheswaran, R.; Sridhar, V.; Barik, K.K.; Merz, B.; Agarwal, A. Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling. Atmosphere 2020, 11, 1252. https://doi.org/10.3390/atmos11111252

37.  Setti, S., Maheswaran, R., Radha, D., Sridhar, V., Barik, K.K., Narasimham, M.L., 2020. Attribution of Hydrologic Changes in a Tropical River Basin to Rainfall Variability and Land-Use Change: Case Study from India. J. Hydrol. Eng. 25, 1–15. https://doi.org/: 10.1061/(ASCE) HE.1943-5584.0001937

38.  Lagudu S, Maheswaran, AmaraSinghe, 2020, Assessment of long term hydrogeological changes and plausible solutions to manage hydrological disasters in the international transboundary Ganges river basin" H2O Journal (accepted)

39.  Setti, S., Rathinasamy, M., Chandramouli, S., 2018. Assessment of water balance for a forest dominated coastal river basin in India using a semi distributed hydrological model. Model. Earth Syst. Environ. 4, 127–140. https://doi.org/10.1007/s40808-017-0402-0

2021-23

40.  AVS Kalyan, DK Ghose, R Thalagapu, RK Guntu, A Agarwal, J Kurths, Maheswaran (2021) Multiscale spatiotemporal analysis of extreme events in the Gomati River basin, India, Atmosphere 12 (4), 480. 

41.  Sourabh et al.. (2021). “Ranking and Characterization of Precipitation Extremes for the past 113 years for Indian western Himalayas",  International Journal of Climatology, Wiley.

42.   Pavan, Tarun, Maheswaran and Agarwal, 2021, Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India, Journal of water and Climate Change. IWA

43.   Sankar, Ghose and Maheswaran, 2021, Machine Learning models for Land use prediction for change dominated river basin, Environmental Modelling and Software, Elsevier (accepted). 

44.   P Ramdas, & Maheswaran Rathinasamy. (2021). Investigating the working efficiency of Natural Treatment Systems for Wastewater Treatement, Water Practice and Technology, IWA Publishing. http://doi.org/10.2166/wpt.2021.049

45.  Ramdas Pinninti, Venkatesh Kasi, L. K. S. V. Prasad Sallangi, Sankar Rao Landa, Maheswaran Rathinasamy, Chandramouli Sangamreddi & Prasada Raju Dandu Radha (2021) Performance of Canna Indica based microscale vertical flow constructed wetland under tropical conditions for domestic wastewater treatment, International Journal of Phytoremediation, DOI: 10.1080/15226514.2021.1962800 

46.  Panday, D.P., Khosa, R., Maheswaran, R. et al. Game theoretic-based modelling of Krishna waters dispute: equilibrium solutions by hypergame analysis. Eur. Phys. J. B 94, 131 (2021). https://doi.org/10.1140/epjb/s10051-021-00135-6 

47.  Panday, D.P., Khosa, R., Maheswaran, R. et al. Game-theoretic-based modelling of Krishna waters dispute: equilibrium solutions by Metagame Analysis. Eur. Phys. J. B 94, 101 (2021). https://doi.org/10.1140/epjb/s10051-021-00107-w 

48.   Karisma, Ravi, Maheswaran and Agarwal, 2022, Quantile-based Bayesian Model Averaging approach towards merging of precipitation products, Journal of Hydrology, 604, 127206. 

49.  Pavan, Maheswaran, Agarwal, Bhattachrya, Deep Learning Models for streamflow forecasting using satellite precipitation products.Journal of Hydroinformatics (2022) 24 (1): 16–37 

50.  Setti, S., Barik, K. K., Merz, B., Agarwal, A., Rathinasamy, M. (2022): Investigating the impact of calibration timescales on streamflow simulation, parameter sensitivity and model performance for Indian catchments. - Hydrological Sciences Journal - Journal des Sciences Hydrologiques, 67, 5, 661-675

51.  Deva Charan Jarajapu, Maheswaran Rathinasamy, Ankit Agarwal, Axel Bronstert, (2023), Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization, Journal of Hydrology, 613.

52.  K Yaswanth, M Kona, SK Andra, M Rathinasamy, (2022), Understanding the impact of changes in land-use land-cover and rainfall patterns on soil erosion rates using the RUSLE model and GIS techniques: A study on the Nagavali River basin, Journal of Water and Climate Change 13 (7), 2648-2670

53.  S Setti, K Yumnam, M Rathinasamy, A Agarwal, (2023)Assessment of satellite precipitation products at different time scales over a cyclone prone coastal river basin in India, Journal of Water and Climate Change 14 (1), 38-65.

54.  AK Bairwa, R Khosa, M Rathinasamy, (2023), Enhanced flushing in long emergent vegetation with two flow parallel interfaces: simulation and predictive modeling at moderate Reynolds number, Stochastic Environmental Research and Risk Assessment, 1-13

55.  Yeditha, Pavan Kumar, G. Sree Anusha, Siva Sai Syam Nandikanti, and Maheswaran Rathinasamy. 2023. "Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach" Water 15, no. 18: 3244. https://doi.org/10.3390/w15183244