Skip to main navigation
Skip to search
Skip to main content
Ben-Gurion University Research Portal Home
Help & FAQ
Link opens in a new tab
Search content at Ben-Gurion University Research Portal
Home
Profiles
Research output
Research units
Prizes
Press/Media
Student theses
Projects
Activities
Datasets
Research Labs
AI-driven predictions of geophysical river flows with vegetation
Sanjit Kumar
, Mayank Agarwal
, Vishal Deshpande
, James R. Cooper
, Khabat Khosravi
, Namal Rathnayake
, Yukinobu Hoshino
, Komali Kantamaneni
, Upaka Rathnayake
Research output
:
Contribution to journal
›
Article
›
peer-review
17
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'AI-driven predictions of geophysical river flows with vegetation'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
AI-based Methods
100%
River Flow
100%
Additive Regression
100%
Flow Velocity
66%
Reduced Error Pruning Tree
50%
Empirical Formula
33%
Machine Learning Algorithms
33%
Korea Superconducting Tokamak Advanced Research (KSTAR)
33%
Hybrid Machine Learning Algorithms
33%
Predictive Ability
16%
Sensitivity Analysis
16%
Forest Trees
16%
Machine Learning
16%
Machine Learning Models
16%
Random Forest
16%
Random Forest Regression
16%
Laboratory Flume
16%
Flume Experiment
16%
Regression Forest
16%
Vegetation Height
16%
Flow Velocity Measurement
16%
River Research
16%
Forecast Performance
16%
Hybrid Machine Learning
16%
Vegetated Rivers
16%
Vegetated Channel
16%
Engineering
River Flow
100%
Flow Velocity
100%
Artificial Intelligence
100%
Machine Learning Algorithm
80%
Random Forest
60%
Learning System
60%
Predictive Capability
20%
Influencing Factor
20%
Input Parameter
20%
Earth and Planetary Sciences
Vegetation
100%
Machine Learning
100%
River Flow
100%
Artificial Intelligence
100%
Flow Velocity
71%
Pruning
42%
Flume Experiment
14%
Velocity Measurement
14%
Agricultural and Biological Sciences
Artificial Intelligence
100%
Learning System
100%
Machine Learning
100%
Influencing Factor
14%