scholarly journals Proposal of a Method to Determine the Correlation between Total Suspended Solids and Dissolved Organic Matter in Water Bodies from Spectral Imaging and Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 159 ◽  
Author(s):  
Maurício R. Veronez ◽  
Lucas Kupssinskü ◽  
Tainá T. Guimarães ◽  
Emilie Koste ◽  
Juarez da Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Organic Matter ◽  
Artificial Neural Networks ◽  
Dissolved Organic Matter ◽  
Suspended Solids ◽  
Spectral Imaging ◽  
Total Suspended Solids ◽  
Water Bodies ◽  
Artificial Neural

scholarly journals Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

2019 ◽  
Vol 11 (9) ◽  
pp. 2580 ◽  
Author(s):  
Tainá T. Guimarães ◽  
Maurício R. Veronez ◽  
Emilie C. Koste ◽  
Eniuce M. Souza ◽  
Diego Brum ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Regression Analysis ◽  
Unmanned Aerial Vehicle ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Water Bodies ◽  
Sampling Point ◽  
Sensing Applications ◽  
Aerial Vehicle

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

Use of Artificial Neural Networks to Predict Levels of Air Pollution and Water Bodies

2019 ◽  
Vol 23 (8) ◽  
pp. 36-41
Author(s):  
A.A. Maslova ◽  
V.M. Panarin ◽  
K.V. Grishakov ◽  
N.A. Rybka ◽  
E.A. Kotova ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Single Layer ◽  
Correct Choice ◽  
Water Bodies ◽  
Back Propagation Algorithm ◽  
Artificial Neural

Describes the process of creating a simple and effective tool for predicting the quality of air and water bodies. Artificial neural networks are an effective tool for predicting the concentrations of suspended particles of heavy metals. The correct choice of input and output data with a clear relationship between them is necessary to obtain reliable results. Emphasis is placed on predictions of heavy metals due to permissible level of these pollutants, which often was exceeded in Tula. For given conditions, the best results are obtained using a single-layer perception with a back propagation algorithm.

Estimation of soil organic matter content by modeling with artificial neural networks

Geoderma ◽  
2019 ◽  
Vol 350 ◽  
pp. 46-51 ◽  
Author(s):  
Mariele Monique Honorato Fernandes ◽  
Anderson Prates Coelho ◽  
Carolina Fernandes ◽  
Matheus Flavio da Silva ◽  
Claudia Campos Dela Marta
Keyword(s):  
Neural Networks ◽  
Organic Matter ◽  
Artificial Neural Networks ◽  
Soil Organic Matter ◽  
Organic Matter Content ◽  
Matter Content ◽  
Soil Organic Matter Content ◽  
Artificial Neural

scholarly journals Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

ACS Omega ◽  
2020 ◽  
Vol 5 (40) ◽  
pp. 26169-26181
Author(s):  
Zeeshan Tariq ◽  
Mohamed Mahmoud ◽  
Mohamed Abouelresh ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Networks ◽  
Organic Matter ◽  
Artificial Neural Networks ◽  
Data Driven ◽  
Thermal Maturity ◽  
Maturity Indices ◽  
Artificial Neural
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