Work

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

Year: 2024

Type: article

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.

Source: HydroResearch

DOI: 10.1016/j.hydres.2024.12.001

Authors: Vinay Mahakur, Vijay Kumar Mahakur, Sandeep Samantaray, Dillip K. Ghose

Institutions: National Institute of Technology Tiruchirappalli, Indian Institute of Management Tiruchirappalli, National Institute of Technology Srinagar, National Institute Of Technology Silchar

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation, Industry, innovation and infrastructure

Open Access status: diamond

Funder: National Institute of Technology, Silchar