02115nam a22002057a 4500999001900000082002100019100001600040245008700056260007700143300001500220502000900235520134900244650002601593650003601619650004601655700003101701856005701732942001201789952010801801  c291959d291959  a551.6bREN/ST PG  aRenjima, N.  aStreamflow prediction using deep learning model in southern peninsular river basin  aVellanikkarabCollege of Climate Change and Environmental Science c2024  axvi, 91 p.  aMSc.3 aHalf of the world's population is living under the highly water-stressed areas,
water availability and management (both in terms of quality and quantity) are going to
be challenging in coming years due to changes in rainfall characteristics and LULC.
Streamflow prediction is important for flood management, reservoir operation, and
agriculture. In the present work, we have trained and tested four data-driven machine
learning models SVM, RF, XGBOOST, and LSTM for streamflow prediction in the
hydrologically similar catchments Meenachil, and Muvattupuzha rivers, situated in
Southern Western Ghats, in the state of Kerala, India. All machine learning models
performed quite well in streamflow prediction with NSE value > 0.56 in both studied
river basins. RF model was found to be outperforming other candidate’s models for
streamflow prediction both during model training and testing periods in daily time scale
and LSTM model outperforms in monthly timescale. We have found that machine
learning performed quite well in hydrologically similar catchments once hyperparameters
are exchanged and resulted in almost similar model accuracy. Machine
learning models found to have significant potential for predictions in ungauged basins,
and thus can be tested in larger sample sets for future potential streamflow modelling
in ungauged basins
  aStreamflow prediction  aSouthern peninsular river basin  aClimate Change and Environmental Science   aRajat Kumar Sharma (Guide)  uhttps://krishikosh.egranth.ac.in/handle/1/5810225709  2ddccTH  001040718THaKAUCLVbKAUCLVcTHESESd2025-04-05l0o551.6 REN/ST PGp176309r2025-04-05 00:00:00yTH