Prediction of runoff at ungauged areas employing interpolation techniques and deep learning algorithm
Vinay Mahakur, Vijay Kumar Mahakur, Sandeep Samantaray, Dillip K. Ghose
Abstract
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.