The data-driven approach as an operational real-time flood forecasting model

2011 ◽  
Vol 26 (19) ◽  
pp. 2878-2893 ◽  
Author(s):  
Phuoc Khac-Tien Nguyen ◽  
Lloyd Hock-Chye Chua
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Driven ◽  
Forecasting Model ◽  
Data Driven Approach

A Data-Driven Approach for Resource Gathering in Real-Time Strategy Games

2012 ◽  
pp. 304-315 ◽  
Author(s):  
Dion Christensen ◽  
Henrik Ossipoff Hansen ◽  
Jorge Pablo Cordero Hernandez ◽  
Lasse Juul-Jensen ◽  
Kasper Kastaniegaard ◽  
...  
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Strategy Games ◽  
Data Driven Approach

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

2020 ◽  
Author(s):  
Jung-Hyun Kim ◽  
Simon I. Briceno ◽  
Cedric Y. Justin ◽  
Dimitri Mavris
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Real Time ◽  
Flight Path ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Real-Time Supply Chain Simulation: A Big Data-Driven Approach

2019 ◽  
Author(s):  
Antonio A.C. Vieira ◽  
Luis M.S. Dias ◽  
Maribel Y. Santos ◽  
Guilherme A.B. Pereira ◽  
Jose A. Oliveira
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Real Time ◽  
Data Driven ◽  
Data Driven Approach ◽  
Supply Chain Simulation

scholarly journals A data-driven approach for real-time full body pose reconstruction from a depth camera

2011 ◽  
Author(s):  
Andreas Baak ◽  
Meinard Muller ◽  
Gaurav Bharaj ◽  
Hans-Peter Seidel ◽  
Christian Theobalt
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Depth Camera ◽  
Data Driven Approach ◽  
Full Body

scholarly journals A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

2017 ◽  
Vol 88 ◽  
pp. 151-167 ◽  
Author(s):  
Xingya Xu ◽  
Xuesong Zhang ◽  
Hongwei Fang ◽  
Ruixun Lai ◽  
Yuefeng Zhang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Model Based ◽  
Filter Approach
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