Renjima, N. creator Rajat Kumar Sharma (Guide) text Vellanikkara College of Climate Change and Environmental Science 2024 monographic xvi, 91 p. Half 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 MSc. Streamflow prediction Southern peninsular river basin Climate Change and Environmental Science 551.6 REN/ST PG https://krishikosh.egranth.ac.in/handle/1/5810225709 https://krishikosh.egranth.ac.in/handle/1/5810225709