Prof. Jagadeesh Anmala

Department of Civil Engineering

water resource engineering and remote sensing, Fluid Mechanics, Environmental Hydraulics and Water Resources, Computational Fluid Dynamics, Water Quality, Surface and Subsurface Hydrology, Soft computing in Civil Engineering, Stream Hydrology, and Environmental Engineering
Birla Institute of Technology & Science, Pilani
Hyderabad Campus
Jawahar Nagar, Kapra Mandal
Dist.-Medchal-500 078
Telangana, India


22. Nagalapalli Satish, Jagadeesh Anmala, K. Rajitha, Murari R.R. Varma 2024, A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India, Ecological Informatics, 102500, ISSN 1574-9541,
21. Jagadeesh Anmala 2023, A variational method for turbulence closure and Reynolds stress, Journal of Technology, volume 11, issue 12, 331-352, DOI:20.18001.JOT.2023.V11I12.23.2980.
20. Turuganti Venkateswarlu, Jagadeesh Anmala 2023, Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using Random Forest, Environment Development Sustainability,
19. Nagalapalli Satish, K. Rajitha, Jagadeesh Anmala, Murari R R Varma 2023, Trophic status estimation of case-2 water bodies of the Godavari River basin using satellite imagery and artificial neural network (ANN), H2Open Journal, Vol 6, No 2, 297-314, doi: 10.2166/h2oj.2023.034
18. Nagalapalli Satish, Jagadeesh Anmala, K. Rajitha, Murari R R Varma 2022, Prediction of stream water quality in Godavari River Basin, India using statistical and artificial neural network models, H2Open Journal, doi: 10.2166/h2oj.2022.019 
17. Hannan Abdul, Jagadeesh Anmala 2021, Classification and prediction of fecal coliform in stream waters using decision trees (DTs) for Upper Green River watershed, Kentucky, USA, Water, 13, 2790.
16. Jagadeesh Anmala, Turuganti Venkateswarlu 2021, Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed, Water Environment Research, 1-14.
15. Surya Prakash Challagulla, Chandu Parimi, Jagadeesh Anmala 2020, Prediction of Spectral Acceleration of a light structure with a flexible secondary system using artificial neural networks, International Journal of Structural Engineering, 10(4), 353-379, DOI: 10.1504/IJSTRUCTE.2020.109857
14. Turuganti Venkateswarlu, Jagadeesh Anmala, Mayank Dharwa 2020, PCA, CCA, and ANN Modeling of Climate and Land-Use Effects on Stream Water Quality of Karst Watershed in Upper Green River, Kentucky, USA, ASCE, Journal of Hydrologic Engineering, 25(6), 05020008-1 to 05020008-11, DOI: 10.1061/(ASCE)HE.1943-5584.0001921.
13. Jagadeesh Anmala, Turuganti Venkateshwarlu 2019, Statistical assessment and neural network modeling of stream water quality observations of Green River watershed, KY, USA, Water Supply (Water, Science, Technology), 19 (6): 1831–1840,
12. Sai Prasanth Duvvuri, Jagadeesh Anmala 2019,  Fecal coliform predictive model using genetic algorithm-based radial basis function neural networks (GA-RBFNNs), Neural Computing and Applications31, pages8393–8409,
11. Boindala Sriman Pankaj, Jagadeesh Anmala 2019, Investigation of latest techniques in carbon sequestration with emphasis on geological sequestration and its effects, MOJ Ecology and Environmental Sciences, 4(1), 7-12.
10. Jagadeesh Anmala, R. H. Mohtar 2015, Cauchy solution of the Kinematic Wave Shallow Water Equations using Square Grid Finite Element Method, ASCE, Journal of Hydrologic Engineering, 20(12), 04015042, DOI: 10.1061/(ASCE)HE.1943-5584.0001241.
9. Jagadeesh Anmala, O.W.Meier, A.J. Meier and S.Grubbs 2015, A GIS and an Artificial Neural Network Based Water Quality Model for a Stream Network in Upper Green River Basin, Kentucky, USA, ASCE, Journal of Environmental Engineering, 141(5), 04014082, DOI: 10.1061/(ASCE)EE. 1943-7870.0000801.

8. Jagadeesh Anmala, R. H. Mohtar 2014, Analytical Evaluation of Amplification Factors, Stability and Error Analysis of Square Finite Element (FE) solution for the Kinematic Wave Shallow Water Equations (KWSWE), ASCE, Journal of Hydrologic Engineering, 19(9), 04014013, DOI: 10.1061/(ASCE)HE.1943-5584.0000950.

7. Jagadeesh Anmala, V. Kapoor 2013, Dynamics of Mixing and Bimolecular Reaction Kinetics in Aquifers, Stochastic Environmental Research and Risk Assessment, 27, 1005-1020. DOI 10.1007/s00477-012-0679-5.
6. Jagadeesh Anmala, V. Kapoor 2012, Mixing and Bimolecular Reaction Kinetics in a Plane Poiseulle Flow, Flow, Turbulence and Combustion, 88, 387-405. DOI: 10.1007/s10494-011-9369-5.
5. Jagadeesh Anmala, R.H.Mohtar 2011, Fourier Stability Analysis of Two-Dimensional Finite Element Schemes for Shallow Water Equations, International Journal of Computational Fluid Dynamics, 25(02), pp.75-94. DOI:10.1080/10618562.2011.560572
4. Jagadeesh Anmala, B. Zhang, and R.S. Govindaraju 2002, Closure to discussion on Comparison of ANNs and other empirical approaches for modeling runoff by V.K. Minocha and M.B. Sonnen, ASCE Journal of Water Resources and Planning and Management, September/October, 381-382.
3. Jagadeesh Anmala, B. Zhang, and R.S. Govindaraju 2000. Comparison of neural network models with other approaches for rainfall-runoff modeling of watersheds, ASCE Journal of Water Resources Planning and Management, vol. 126, no.3, 156-166.
2. Kapoor, V., Jagadeesh Anmala 1998, Lower bounds on scalar dissipation rate in bounded rectilinear flows, Flow, Turbulence and Combustion, vol. 60(2), 125-156.
1. Mukherjee, A., J.M. Deshpande, Jagadeesh Anmala 1996, Prediction of buckling stress using artificial neural networks, ASCE, Journal of Structural Engineering, vol. 122, no.11, 1385-1387.
1. Jagadeesh Anmala (2010). Mixing and Bimolecular Reactions in Heterogeneous Porous Media Flows: Transport, Mixing and Reactions, LAP Lambert Academic Publishing, Germany, ISBN 978-3-8383-4383-9, pp. 196.

2. Jagadeesh Anmala (2010). Rainfall-Runoff Modeling Using Artificial Neural Networks and Physically-based Model: Theory, Simulation and Results, LAP Lambert Academic Publishing, Germany, ISBN 978-3-8383-8339-2, pp. 200.
16.  Venkateswarlu, T., J. Anmala, Application of Random Forest Model in The Prediction of River Water Quality, 7th International Congress on Information and Communication Technology (ICICT 2022) conference held on 21-22 Febreuary 2022 in London.
15. Satish, N., J. Anmala, Murari R R Varma, K. Rajitha, An approach to determine land use factors to study their influence on stream water quality, 26th HYDRO 2021 International Conference on Hydraulics, Water Resources and Coastal Engineering,  held at SVNIT Surat, Gujarat, India, December 23-25 2021. 
14. Venkateswarlu, T., Abdul Hannan, Maitreyee Talnikar, J. Anmala, Significance of classification and regression tree (CART) models in the prediction of river water quality, XXV HYDRO 2020 International Conference (Hydraulics, Water Resources & Coastal Engineering) held at N.I.T Rourkela, 26-28 March 2021.  
13. Venkateswarlu, T., R. Agrawal, B. Purna Srivatsa, J. Anmala, Prediction of river water quality of upper green river watershed, Kentucky, USA using cluster-based neural network models, XXIV HYDRO 2019 International Conference (Hydraulics, Water Resources & Coastal Engineering) held at Osmania University, 18-20 December 2019, 2913-2923. 
12. Sanchari Thakur, Satyendra Tripathi, A. Vasan, J. Anmala,  Reservoir Inflow forecasting Using Artificial Neural Networks, International Conference on Modeling Tools for Sustainable Water Resource Management, IIT Hyderabad, Dec 28-29, 2014.
11.  Anmala J., R.H.Mohtar, Time-step Criteria and Fourier or von Neumann Analysis of Two-Dimensional Finite Element Model for Shallow water equations, ASAE Meeting, Tampa, Florida, USA, July 17-20, 2005.
10.  Mohtar R.H., J. Anmala, Error Analysis of Control Volume Finite Element (CVFE) method for Shallow water equations, ASAE Meeting, Tampa, Florida, USA, July 17-20, 2005.
9.  Meier, O.W., S. Grubbs, A.J. Meier, and J.Anmala, Analysis of Landuse, Climate, Stream Buffers, and Water Quality in the Upper Green River Basin of Kentucky, Bulletin of the Ecological Society of America, Bowling Green, KY, USA, 2003.
8.  Anmala, J., V.Kapoor, Mixing and bimolecular reaction kinetics in stratified flows, AGU, San Francisco, CA, USA, December 1999.
7.  Kapoor, V., J. Anmala, Chemical transformation kinetics in subsurface flows, AGU, San Francisco, CA, USA, December 1999.
6. Anmala, J., K.V.Nedunuri, R.S.Govindaraju, J.K.Koelliker, Neural Networks for prediction of watershed runoff, First ASCE International Congress on Water Resources Engineering, San Antonio, Texas, USA, August 14-18, 1995.
5.  Anmala, J., R.S.Govindaraju, Estimation of soil hydrualic properties from particle size distribution using ANNs, Conference on Hazardous Waste Research, Manhattan, Kansas, USA, May 23-24, 1995.
4.  R.S.Govindaraju, J.Anmala, K.V.Nedunuri, and J.K.Koelliker, Use of neural networks in predicting the nonlinear rainfall-runoff relationships for small watersheds, XX General Assembly, European Geophysical Society, Hamburg, Germany, April 3-7, 1995.
3. Anmala, J., R.S.Govindaraju, J.K.Koelliker and K.V.Nedunuri, Neural Networks for continuous prediction of watershed runoff, Water and the Future of Kansas Conference, Manhattan, KS, USA, February 27-28, 1995.
2.  R.S.Govindaraju, J.K.Koelliker, V.Davis and J.Anmala, Development of a watershed scale surface flow model, Water and the Future of Kansas Conference, Manhattan, KS, USA, February 27-28, 1995.
1.  Deshpande, J.M., J.Anmala, Characterization of mechanical properties of materials using artificial neural networks, 38th Congress of Indian Society of Theoretical and Applied Mechanics (ISTAM), I.I.T Kharagpur, India, December 1993.
1. Lindell Ormsbee, J. Anmala, S. E. Myers.  Total Maximum Daily Load (TMDL) Development for Eagle Creek. June 2004
2.  Govindaraju R.S., J.K. Koelliker, J. Anmala. Development of a watershed-scale physically-based surface flow model - year II, Technical Report No. G2020-03, Kansas Water Resources Institute, KSU, Manhattan, KS, May 1996. 
 3. Govindaraju R.S., J.K. Koelliker, J. Anmala. Development of a watershed-scale physically-based surface flow model - year I, Technical Report No. G2020-03, Kansas Water Resources Research Institute, KSU, Manhattan, KS, May 1995.