Prediction of runoff at ungauged areas employing interpolation techniques and deep learning algorithm

Most river basins across the world are ungauged, and just a handful are gauged. As a result, predicting runoff in an unmeasured watershed is a difficult problem for the researchers. This research takes into account the tropical monsoon region, which is primarily covered by mountains and has a changing climate. This research is also carried out by creating a model with a machine learning technique that comprises Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The hybrid model considerably improves runoff forecast accuracy, with the CNN-LSTM model reaching an overall accuracy of 99.29 % across many datasets. The study uses 25 years of meteorological data from gauged stations to calculate runoff predictions for four ungauged sites: Katigora, Subhang, Sonai, and Morang. The findings highlight the necessity of combining machine learning and classical approaches to improve flood forecasting skills, which are critical for successful water resource management in flood-prone areas. This novel technique not only fills a vital vacuum in hydrological research, but it also has practical implications for catastrophe risk mitigation initiatives worldwide. • Addresses the critical challenge of runoff prediction in ungauged river basins, particularly in tropical monsoon regions. • Utilizes Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced prediction accuracy. • Employs Inverse Distance Weighting (IDW) and spline methods to estimate rainfall data from gauged stations. • CNN-IDW shows superior accuracy with an R 2 value of 0.982, indicating 98.2 % accuracy. • Provides valuable insights for flood management and disaster risk reduction in vulnerable regions.