AI-driven predictions of geophysical river flows with vegetation.

Kumar, Sanjit, Agarwal, Mayank, Deshpande, Vishal, Cooper, James R ORCID: 0000-0003-4957-2774, Khosravi, Khabat, Rathnayake, Namal, Hoshino, Yukinobu ORCID: 0000-0001-5285-8007, Kantamaneni, Komali and Rathnayake, Upaka ORCID: 0000-0002-7341-9078 (2024) AI-driven predictions of geophysical river flows with vegetation. Scientific reports, 14 (1). 16368-. ISSN 2045-2322, 2045-2322

Access the full-text of this item by clicking on the Open Access link.

Official URL: https://doi.org/10.1038/s41598-024-67269-2

Abstract

In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques-Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R<sup>2</sup> = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.

Item Type:	Article
Uncontrolled Keywords:	Alluvial channel, Empirical equations, Flow velocity, Machine learning models, Vegetation
Depositing User:	Symplectic Admin
Date Deposited:	24 Jul 2024 13:22
Last Modified:	08 Dec 2024 03:04
DOI:	10.1038/s41598-024-67269-2
Open Access URL:	https://doi.org/10.1038/s41598-024-67269-2
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3183137