Frequency interpolation method for increasing speed of long‐range broadband parabolic equation (PE) calculation

1995 ◽  
Vol 97 (5) ◽  
pp. 3235-3235
Author(s):  
K. D. Heaney ◽  
W. A. Kuperman
Keyword(s):  
Parabolic Equation ◽  
Long Range ◽  
Interpolation Method

Metamodel predictions for long range speech intelligibility using parabolic equation calculations.

2010 ◽  
Vol 127 (3) ◽  
pp. 2037-2037
Author(s):  
Amanda D. Hanford ◽  
Andrew R. Barnard ◽  
Michael L. Jonson ◽  
Jay D. Martin
Keyword(s):  
Parabolic Equation ◽  
Long Range ◽  
Speech Intelligibility

Model-data comparison of the parabolic equation approximation for long-range propagation, time spreading, and channel bandwidth estimation

2019 ◽  
Vol 146 (4) ◽  
pp. 3036-3036
Author(s):  
Paul C. Hines ◽  
Terry Deveau ◽  
Elizabeth Kusel ◽  
David R. Barclay ◽  
Dainis Nams ◽  
...  
Keyword(s):  
Parabolic Equation ◽  
Long Range ◽  
Propagation Time ◽  
Bandwidth Estimation ◽  
Model Data ◽  
Data Comparison ◽  
Channel Bandwidth

Ray theory versus the parabolic equation in a long‐range ducted environment

1985 ◽  
Vol 78 (1) ◽  
pp. 176-189 ◽  
Author(s):  
A. Tolstoy ◽  
D. H. Berman ◽  
E. R. Franchi
Keyword(s):  
Parabolic Equation ◽  
Long Range ◽  
Ray Theory ◽  
Theory Versus

Comparison of data and parabolic equation solutions for long‐range pulse propagation

1993 ◽  
Vol 93 (4) ◽  
pp. 2426-2426
Author(s):  
Kevin D. Heaney ◽  
W. A. Kuperman ◽  
B. Edward McDonald ◽  
Michael D. Collins
Keyword(s):  
Parabolic Equation ◽  
Long Range ◽  
Pulse Propagation

scholarly journals DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 157-158
Author(s):  
J. Rhee ◽  
J. Im ◽  
S. Park
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Long Range ◽  
Initial Conditions ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Standardized Precipitation Index ◽  
Learning Models ◽  
Drought Forecasting ◽  
Machine Learning Models

The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

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