Research data supporting the manuscript “A machine learning framework for reliable prediction of open channel flow depth and velocity for hydraulic engineering applications”

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Authors: Mohd Nadim Ahmed
Cited by: 0

Abstract

This dataset contains the input and output variables, processed data, and model-ready datasets used to develop and validate machine learning models for reliable prediction of open channel flow depth and velocity in hydraulic engineering applications. The data supports the manuscript entitled “A machine learning framework for reliable prediction of open channel flow depth and velocity for hydraulic engineering applications” submitted to Catena. All files are organized and described in the accompanying Machine learning optimized data and results file.

Fields of Study & Subjects

Hydraulic flow and structures Hydrological Forecasting Using AI Hydrology and Sediment Transport Processes

Publication details

Journal / source
Mendeley Data
Publication date
19 January 2026
Document type
Dataset