Dr. Nagendram Veerapaga
@sru.edu.in
Assistant Professor
School of Agriculture, SR University
𝐃𝐫. 𝐍𝐚𝐠𝐞𝐧𝐝𝐫𝐚𝐦 𝐕𝐞𝐞𝐫𝐚𝐩𝐚𝐠𝐚 is presently working as an Assistant Professor at the School of Agriculture, SR University, Telangana, India. He completed his Ph.D. in environmental hydraulics from the Civil and Environmental Engineering Department, Graduate School of Urban Environmental Sciences, Tokyo Metropolitan University, Tokyo, Japan. M.Tech in Land and Water Resources Engineering from Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, West Bengal, India and B.Tech in Agricultural Engineering from Acharya N G Ranga Agricultural University, Guntur, Telangana, India.
His research interests include Optimization of Land and Water Resources for sustainable development of agriculture, Water Balance Studies in Small Experimental Watersheds, Impact of Climate change on Hydrology, and Coastal Modeling – Emphasis on Estuarine Hydrodynamic model.
EDUCATION
𝐁.𝐓𝐞𝐜𝐡 - 𝐀𝐠𝐫𝐢𝐜𝐮𝐥𝐭𝐮𝐫𝐚𝐥 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (2014) from Acharya N G Ranga Agricultural University
𝐌.𝐓𝐞𝐜𝐡 - 𝐋𝐚𝐧𝐝 𝐚𝐧𝐝 𝐖𝐚𝐭𝐞𝐫 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (2016) from Agriculture and Food Engineering Department, IIT Kharagpur
𝐏𝐡.𝐃 - 𝐂𝐢𝐯𝐢𝐥 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (2019) from Dept. of Civil and Environmental Engineering, Tokyo Metropolitan University, Tokyo, Japan
RESEARCH INTERESTS
Optimization of Land and Water Resources for sustainable development of the agriculture
Water Balance Studies in Small Experimental Watersheds
Impact of Climate change on Hydrology
Coastal Modeling – Emphasis on Estuarine Hydrodynamic model
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chunchu Suchith Kumar, D. P. Divya Vani, K. Damodar, Geetha Manoharan, and Nagendram Veerapaga
IGI Global
Climate change endangers humanity. Global warming impacts everyone. Globally, people are working on climate change mitigation and scientific and technological solutions, regardless of their development level. AI will greatly impact climate change mitigation. Artificial intelligence data analysis predicts weather, manages energy, and analyzes industrial pollution. Blockchain AI improves sustainability efficiency, traceability, and transparency. The AI-driven supply management system for climate change mitigation optimizes and simplifies supply chains. To reduce carbon emissions and production and distribution environmental consequences. Climate change mitigation, food security, and environmental sustainability can benefit from AI-driven sustainable farming. Deforestation and poaching cause climate change. Artificial intelligence can predict forest fires, stop poaching, and track biodiversity to combat climate change. Wildlife conservation and reforestation can improve. AI could improve sustainability and environmental responsibility in transportation.
Kirana Somsook, Neriezza A. Olap, Maurice A. Duka, Nagendram Veerapaga, Tetsuya Shintani, and Katsuhide Yokoyama
Elsevier BV
Nagendram Veerapaga, Tetsuya Shintani, Gubash Azhikodan, and Katsuhide Yokoyama
Springer Singapore
In order to discuss the variation of salinity intrusion and mixing types in terms of estuary length, width, depth, and bathymetry, a total of 31 numerical experiments were carried out with a conceptual estuary by using a three-dimensional hydrodynamic simulator, Fantom-Refined. Since sand bars are formed in a river channel, and cyclic variation of the river bed height is found in longitudinal direction, sinusoidal wavy shapes were considered for rough bottom cases to represent the river bed with four different wave amplitudes (0.1, 0.2, 0.3 and 0.4 m) and wavelengths (350, 700, 1400 and 2800 m). In the cases of constant tidal range and discharge with the flat bottom, salinity intrusion length was decreased with increase in estuary length, and mixing condition was changed from salt wedge to well mixed type. On the other hand, salinity intrusion length was increased with increase in width of the channel under constant discharge. Further, the salinity mixing condition was changed from well mixed to salt wedge with the increase in depth of the channel. The salinity intrusion length was increased in the case of funnel shaped estuary when compared with the rectangular shaped estuary. Wavy bottom of the channel had less intrusion length compared with the flat bottom of the constant tidal range and discharge as the bottom friction reduced the velocity of the gravitational flow as well as enhanced vertical mixing. For the constant wavelength, the salinity intrusion length was decreased with increase in wave amplitude. On the other hand, for constant wave amplitude, the salinity intrusion length was increased with increase in wavelength.
Nagendram Veerapaga, Gubash Azhikodan, Tetsuya Shintani, Naoya Iwamoto, and Katsuhide Yokoyama
Elsevier BV