Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network

2019 ◽  
Vol 43 (16) ◽  
pp. 2493-2511
Author(s):  
Hua Zhang ◽  
Shunde Yin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
In Situ Stress ◽  
Borehole Breakouts ◽  
Artificial Neural

scholarly journals A Portable in-situ Near-infrared LEDs-based Soil Nitrogen Sensor Using Artificial Neural Network

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Nur Aisyah Syafinaz Suarin ◽  
◽  
Seng Chia Kim ◽  
Siti Fatimah Zaharah Mohamad Fuzi ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Nitrogen ◽  
Near Infrared ◽  
Artificial Neural

scholarly journals Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 129
Author(s):  
Jae-Cheol Jang ◽  
Eun-Ha Sohn ◽  
Ki-Hong Park ◽  
Soobong Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Korean Peninsula ◽  
Potential Evapotranspiration ◽  
Weather Prediction ◽  
Spatiotemporal Variability ◽  
Artificial Neural ◽  
Using Data

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (R) of 0.649 mm day−1, −0.134 mm day−1, and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson’s R of 1.730 mm day−1, 1.212 mm day−1, and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions.

Modeling of wear process parameters of in-situ RZ5-10wt%TiC Composite using artificial neural network

2018 ◽  
Vol 5 (11) ◽  
pp. 24124-24132
Author(s):  
Deepak Mehra ◽  
S.V. Sujith ◽  
M.M. Mahapatra ◽  
S.P. Harsha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Wear Process ◽  
Artificial Neural
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