scholarly journals Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

2021 ◽  
Vol 004 (02) ◽  
pp. 115-126
Author(s):  
Aprianto Nomleni ◽  
Ery Suhartanto ◽  
Donny Harisuseno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tropical Rainfall Measuring Mission ◽  
Polynomial Equation ◽  
Observation Data ◽  
Artificial Neural Network Analysis ◽  
Backpropagation Method ◽  
Discharge Model ◽  
Artificial Neural ◽  
The Relationship

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%

Artificial Neural Network Analysis Method of Concrete Performance Based on Virtual Data Samples

2010 ◽  
Vol 34-35 ◽  
pp. 462-466
Author(s):  
Jun Wei Song ◽  
Yan Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Forecast Performance ◽  
Virtual Samples ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural ◽  
Predicted Values ◽  
The Relationship

The relationship between concrete performance and influence factors is uncertain and nonlinear. Accordingly, present BP neural network and virtual samples are presented to predict concrete performance in this paper. At first neural network and matters which need attention are introduced, And frost resistance forecasting model and impermeability model are built up, which are three-tier BP neural network of 6-13-2,4-9-1.The results show that the predicted values are ideal, and artificial neural network as one of the methods to forecast performance of concrete is appropriate.

scholarly journals Analisis Metode Backpropagation Memprediksi Penerimaan Santri/Wati di Pondok Pesantren Modern Al-Kautsar

2021 ◽  
Vol 6 (1) ◽  
pp. 140
Author(s):  
Parta Wijaya ◽  
Rahmat Widiya Sembiring ◽  
Saifullah S
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Direct Observation ◽  
Boarding School ◽  
Observation Data ◽  
Process Data ◽  
Target Value ◽  
Backpropagation Method ◽  
Artificial Neural

The development of technology today with the existence of artificial intelligence, conducted research to prove the Backpropagation method can predict students / wati in the modern boarding school Al-Kautsar. Artificial neural network is a method that is able to perform mathematical processes in predicting santri / wati. Bacpropagation algorithm is used to process data that is implemented with Matlab. Where data is collected through direct observation. Data is grouped by majors. The results obtained from the Matlab test performace and epoch values of each architecture are not the same as the results of the tests are displayed in the form of a graph comparing the target value with the research and testing process. The results of this study provide information on the modern Al-Kautsar boarding school on the number of registrants in 2020

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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