scholarly journals Introducing a Novel Digital Elevation Model Using Artificial Neural Network Algorithm

2020 ◽  
Vol 22 (2) ◽  
pp. 47-51
Author(s):  
Saeed Behzadi ◽  
Amin Jalilzadeh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The Earth ◽  
Digital Elevation ◽  
Distance Weighting ◽  
Comprehensive Survey ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Elevation Model

Elevation is a basic information of the earth, and different elevation models are provided to better understanding the earth and its different functions. However, it is not always possible to conduct a comprehensive survey in big areas and calculate all surface points. The best way is survey some points, then the elevation estimation is done using these points in each part of study area. The purpose of this paper is to use interpolation methods to estimate elevation. In such cases, different methods are used to interpolate and estimate points with an uncertain height. In this paper, the three usual methods are chosen and introduced then their performance are compared. These methods including: Inverse Distance Weighting (IDW), the Krige method or Kriging, and Artificial Neural Network (ANN). The results show that Artificial Intelligence with RMS = 5.9m is better in compare to Kriging with RMS = 7.2 and IDW with RMS = 9. The obtained result presents that in despite of its convenience, ANN provides DEMs with minimum errors.

scholarly journals Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

2012 ◽  
Vol 25 (2) ◽  
pp. 165-182 ◽  
Author(s):  
Imran Ahmad Dar ◽  
K. Sankar ◽  
Mithas Ahmad Dar ◽  
Mrinmoy Majumder
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Modeling ◽  
Chemical Parameters ◽  
Underground Waters ◽  
Distance Weighting ◽  
Distance Weighted ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Inverse Distance

The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.

scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

scholarly journals Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1551
Author(s):  
Dong Eon Kim ◽  
Jiandong Liu ◽  
Shie-Yui Liong ◽  
Philippe Gourbesville ◽  
Günter Strunz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Truth ◽  
Observation Data ◽  
Ground Truth Data ◽  
Artificial Neural ◽  
Improvement Scheme ◽  
Flood Planning ◽  
Artificial Neural Network Ann ◽  
Elevation Model

The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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