Precipitation data merging using artificial neural networks

2009 ◽  
Author(s):  
Qian Du
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Precipitation Data ◽  
Data Merging ◽  
Artificial Neural

Precipitation data fusion using vector space transformation and artificial neural networks

2010 ◽  
Vol 31 (10) ◽  
pp. 1184-1200 ◽  
Author(s):  
Anish C. Turlapaty ◽  
Valentine G. Anantharaj ◽  
Nicolas H. Younan ◽  
F. Joseph Turk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Fusion ◽  
Vector Space ◽  
Precipitation Data ◽  
Space Transformation ◽  
Artificial Neural

scholarly journals Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1653 ◽  
Author(s):  
Amir Hossein Salimi ◽  
Jafar Masoompour Samakosh ◽  
Ehsan Sharifi ◽  
Mohammad Reza Hassanvand ◽  
Amir Noori ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heavy Precipitation ◽  
Imperialist Competitive Algorithm ◽  
Absolute Error ◽  
Precipitation Data ◽  
Ann Model ◽  
Satellite Precipitation ◽  
Artificial Neural ◽  
Residual Correction

Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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