Details of Book Chapters Published


  1. Khatun, A., Kumar, A., Chatterjee, C. (2026). “Exploring the efficacy of different variants of long short-term memory networks in daily streamflow forecasting” In: Advances in Chemical Pollution, Environmental Management and Protection, Elsevier, https://doi.org/10.1016/bs.apmp.2025.11.005
  2. Swain, S.S., Mishra, A., Chatterjee, C., and Levy, M.C. (2025). “A hydrologic modeling assessment of future water scarcity in the Baitarani river basin”. In: Nanda, A., Gupta, P.K., Gupta, V., Jha, P.K., Dubey, S.K. (eds) Navigating the Nexus: Hydrology, Agriculture, Pollution and Climate Change, Water Science and Technology Library, vol 102, pp. 499-526, Springer, Cham. https://doi.org/10.1007/978-3-031-76532-2_20
  3. Mondal, K., Gupta, V., Chatterjee, C., Singh, R. (2025). “Analysing the impact of climate change on crop yield and its consequences for the Water-Energy-Food nexus in an Indian river basin”. In: Nanda, A., Gupta, P.K., Gupta, V., Jha, P.K., Dubey, S.K. (eds) Navigating the Nexus: Hydrology, Agriculture, Pollution and Climate Change, Water Science and Technology Library, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-031-76532-2_8
  4. Mailapalli, A., Khose, S.B., Dubey, S., Mailapalli, D.R., Chatterjee, C., and Raghuwanshi, N.S. (2024). “Prediction of Pest Infestation in Tea Leaves Using Machine Learning Models”. In: Saha, H.N., Ray, H., Bradford, P.G. (eds.), International Conference on Systems and Technologies for Smart Agriculture, Springer Proceedings in Information and Communication Technologies, pp. 275-287, https://doi.org/10.1007/978-981-97-5157-0_23
  5. Choubey, S., Dey, T., Akuli, A., Mailapalli, D. R., Chatterjee, C., Bej, G., Pal, A., and Ghosh, A. (2024). “A comparative study of feature detection and description algorithms for computer vision applications: assessing accuracy and computational efficiency”. In Saha, H.N., Ray, H., Bradford, P.G. (eds.), International Conference on Systems and Technologies for Smart Agriculture, Springer Proceedings in Information and Communication Technologies, pp. 51-62, https://doi.org/10.1007/978-981-97-5157-0_5.
  6. Sahoo B., Nanda T., Chatterjee C. (2022). “Flood forecasting using simple and ensemble Artificial Neural Networks”. In: Pandey A., Chowdary V.M., Behera M.D., Singh V.P. (eds) Geospatial Technologies for Land and Water Resources Management. Water Science and Technology Library, vol 103. Springer, Cham.
  7. Tiwari, M. K., and Chatterjee, C. (2018). “Flood forecasting and uncertainty assessment using wavelet- and bootstrap-based neural networks”, Chapter 4 in Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering, IGI Global, 74-93.
  8. Das, D. M., Singh, R., Kumar, A., Mailapalli, D. R., Mishra, A., and Chatterjee, C. (2016). “A multi-model ensemble approach for stream flow simulation”, Chapter 4 in Modeling Methods and Practices in Soil and Water Engineering, edited by Panigrahi, B and Goyal, CRC Press, 72-100.
  9. Kumar, R., and Chatterjee, C. (2011). “Development of regional flood frequency relationships for gauged and ungauged catchments using L-moments”, in IN EXTREMIS: Disruptive Events and Trends in Climate and Hydrology, edited by J. P. Kropp and H. J. Schellnhuber, Springer, 105-127.