Temperatures and normalized difference vegetation index forecasting in the tropical rainforest: Hala-Bala wildlife sanctuary, Thailand using artificial neural networks

2019 ◽  
Author(s):  
S. Chuenjai ◽  
C. Rosnee ◽  
C. Sittidet
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainforest ◽  
Wildlife Sanctuary ◽  
Artificial Neural

scholarly journals Soil salinity mapping utilizing sentinel-2 and neural networks

2018 ◽  
Author(s):  
R.S. Morgan ◽  
M. Abd El-Hady ◽  
I.S. Rahim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Salinity ◽  
Vegetation Index ◽  
High Spectral Resolution ◽  
Coefficient Of Determination ◽  
Infrared Band ◽  
Artificial Neural ◽  
Reflectance Data ◽  
Sentinel 2

Soil salinity is the most important soil property that affects the agriculture productivity. Periodical monitoring of its status is considered a crucial factor in the selection of appropriate agricultural practices to attain a sustainable production. The availability of remote sensing data processed by a somewhat novel method such as artificial neural networks (ANN) offer a potential solution that could easily and affordably replace the in-site monitoring methods. The aim of this work is to use high spectral resolution Sentinel-2 (S2) data for soil salinity prediction utilizing neural networks. The study evaluated three approaches in processing the S2 data for inclusion in the artificial neural network for soil salinity prediction. These approaches included S2 spectral reflectance data, spectral indices and principal component analysis (PCA) of the S2 data. The results revealed that a combination of these approaches including the reflectance data of band 11(shortwave infrared band) of S2, the normalized differential vegetation index (NDVI) and the second PCA (dominated by the near infrared band) gave the best performance when used as input when designing the artificial neural networks to predict the soil salinity. The overall accuracy of this approach has a coefficient of determination (R2) of 0.94 between the actual and predicted soil salinity.

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

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
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