scholarly journals Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

2021 ◽  
Vol 13 (24) ◽  
pp. 5156
Author(s):  
Jie Wang ◽  
Duanyang Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Satellite Data ◽  
Tibet Plateau ◽  
Precipitation Threshold ◽  
Hidden Layer ◽  
Qinghai Tibet Plateau ◽  
Sample Ratio

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

scholarly journals Optimasi Waktu Akuisisi Data Satelit Noaa18 Menggunakan Jaringan Syaraf Tiruan Backpropagation

AVITEC ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Anis Maghfirotul Habibah ◽  
Ibrahim Nawawi ◽  
Ika Setyowati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Data ◽  
Elevation Angle ◽  
Earth Station ◽  
Optimization System ◽  
Time Optimization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Earth stations are built to monitor the presence of satellites starting from satellite data, monitoring satellites, and carry out orders and corrections if needed. On the earth station there is a satellite data receiving antenna, the more elevation angle of the current satellite data receiver antenna can affect the time duration of the satellite data. The purpose of this research is to apply the Artificial Neural Network (ANN) method to design a time optimization system for satellite data at the LAPAN Pekayon earth station, East Jakarta. The data used as input is the elevation angle. The benefit of this research is expected to make it easier for operators and technicians to measure the time optimization of satellite data at earth stations. The best training results with learning rate = 0.2, error = 0.0001, max. epoch = 100000, neuron hidden layer = 15. The MSE value obtained is 0.0001 reaching the goal at epoch 68810. Regret the training / training reverse sequence reaches 0.99878. The best test result is to use learning speed 0.2 hidden layer neurons 15 comparison of training data = 54 and test data = 18. The accurate result is exactly the same as the specified error, namely 0.0001. The difference in the average target duration is 3 seconds compared to the ANN target. Artificial Neural Network (ANN) with the back propagation method of training function gradient descent (traingd), was successfully used to an optimization system for satellite data acquisition time at earth stations.

Response of phosphorus fractions to land-use change followed by long-term fertilization in a sub-alpine humid soil of Qinghai–Tibet plateau

2018 ◽  
Vol 19 (3) ◽  
pp. 1109-1119 ◽  
Author(s):  
Xiaolei Sun ◽  
Meng Li ◽  
Guoxi Wang ◽  
Marios Drosos ◽  
Fulai Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Phosphorus Fractions ◽  
Tibet Plateau ◽  
Qinghai Tibet Plateau

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

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