scholarly journals Strengths and limitations of the NATALI code for aerosol typing from multiwavelength Raman lidar observations

2018 ◽  
Vol 176 ◽  
pp. 05005 ◽  
Author(s):  
Doina Nicolae ◽  
Camelia Talianu ◽  
Jeni Vasilescu ◽  
Victor Nicolae ◽  
Iwona S. Stachlewska
Keyword(s):  
Neural Networks ◽  
Optical Parameters ◽  
Raman Lidar ◽  
Extinction Data ◽  
Aerosol Model ◽  
Predominant Component ◽  
Lidar Observations ◽  
Aerosol Type ◽  
Made In

A Python code was developed to automatically retrieve the aerosol type (and its predominant component in the mixture) from EARLINET’s 3 backscatter and 2 extinction data. The typing relies on Artificial Neural Networks which are trained to identify the most probable aerosol type from a set of mean-layer intensive optical parameters. This paper presents the use and limitations of the code with respect to the quality of the inputed lidar profiles, as well as with the assumptions made in the aerosol model.

scholarly journals Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation

2014 ◽  
Vol 7 (3) ◽  
pp. 757-776 ◽  
Author(s):  
A. C. Povey ◽  
R. G. Grainger ◽  
D. M. Peters ◽  
J. L. Agnew
Keyword(s):  
Boundary Layer ◽  
Prior Knowledge ◽  
Optimal Estimation ◽  
Real Data ◽  
Raman Lidar ◽  
Ash Layer ◽  
Lidar Ratio ◽  
Aerosol Backscatter ◽  
Lidar Observations

Abstract. Optimal estimation retrieval is a form of nonlinear regression which determines the most probable circumstances that produced a given observation, weighted against any prior knowledge of the system. This paper applies the technique to the estimation of aerosol backscatter and extinction (or lidar ratio) from two-channel Raman lidar observations. It produces results from simulated and real data consistent with existing Raman lidar analyses and additionally returns a more rigorous estimate of its uncertainties while automatically selecting an appropriate resolution without the imposition of artificial constraints. Backscatter is retrieved at the instrument's native resolution with an uncertainty between 2 and 20%. Extinction is less well constrained, retrieved at a resolution of 0.1–1 km depending on the quality of the data. The uncertainty in extinction is > 15%, in part due to the consideration of short 1 min integrations, but is comparable to fair estimates of the error when using the standard Raman lidar technique. The retrieval is then applied to several hours of observation on 19 April 2010 of ash from the Eyjafjallajökull eruption. A depolarising ash layer is found with a lidar ratio of 20–30 sr, much lower values than observed by previous studies. This potentially indicates a growth of the particles after 12–24 h within the planetary boundary layer. A lower concentration of ash within a residual layer exhibited a backscatter of 10 Mm−1 sr−1 and lidar ratio of 40 sr.

scholarly journals A Neural Network Aerosol Typing Algorithm Based on Lidar Data

2018 ◽  
Author(s):  
Doina Nicolae ◽  
Jeni Vasilescu ◽  
Camelia Talianu ◽  
Ioannis Binietoglou ◽  
Victor Nicolae ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Synthetic Data ◽  
Optical Data ◽  
Lidar Data ◽  
Optical Parameters ◽  
Physical Content ◽  
Large Variability ◽  
Aerosol Model ◽  
Aerosol Type

Abstract. Atmospheric aerosols play a crucial role in the earth system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols depicted. One such technique for aerosol typing is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral data. The algorithm has been adjusted for running on the EARLINET 3ß + 2a (+1d) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate datasets containing or not the measured linear particle depolarization ratios (LPDR): a) identification of mixtures from 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and b) identification of 5 predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable answer. The whole algorithm has been integrated into a Python code. The main issue with the approached used in NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. The algorithm has side-applications, for example, to test the quality of the optical data and identify incorrect calibration or incorrect cloud screening. Blind tests on EARLINET data samples showed the capability of this tool to retrieve the aerosol type from a large variety of data, with different quality and physical content.

scholarly journals A neural network aerosol-typing algorithm based on lidar data

2018 ◽  
Vol 18 (19) ◽  
pp. 14511-14537 ◽  
Author(s):  
Doina Nicolae ◽  
Jeni Vasilescu ◽  
Camelia Talianu ◽  
Ioannis Binietoglou ◽  
Victor Nicolae ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Optical Data ◽  
Lidar Data ◽  
Optical Parameters ◽  
Physical Content ◽  
Large Variability ◽  
Aerosol Model ◽  
Earth’S System ◽  
Aerosol Type

Abstract. Atmospheric aerosols play a crucial role in the Earth's system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed lidar-based techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols. One such technique is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3β+2α(+1δ) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters, calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate data sets containing (or not) the measured linear particle depolarization ratios (LPDRs): (a) identification of 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable aerosol type. The whole algorithm has been integrated into a Python application. The limitation of NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. Additional applications of NATALI are feasible, e.g. testing the quality of the optical data and identifying incorrect calibration or insufficient cloud screening. Blind tests on EARLINET data samples showed the capability of NATALI to retrieve the aerosol type from a large variety of data, with different levels of quality and physical content.

scholarly journals Retrieval of aerosol backscatter, extinction, and lidar ratio from Raman lidar with optimal estimation

2013 ◽  
Vol 6 (5) ◽  
pp. 9297-9346
Author(s):  
A. C. Povey ◽  
R. G. Grainger ◽  
D. M. Peters ◽  
J. L. Agnew
Keyword(s):  
Boundary Layer ◽  
Linear Regression ◽  
Optimal Estimation ◽  
Real Data ◽  
Raman Lidar ◽  
Ash Layer ◽  
Lidar Ratio ◽  
Aerosol Backscatter ◽  
Lidar Observations

Abstract. Optimal estimation retrieval is a form of non-linear regression which determines the most probable circumstances that produced a given observation, weighted against any prior knowledge of the system. This paper applies the technique to the estimation of aerosol backscatter and extinction (or lidar ratio) from two-channel Raman lidar observations. It produces results from simulated and real data consistent with existing Raman lidar analyses and additionally returns a more rigorous estimate of its uncertainties while automatically selecting an appropriate resolution without the imposition of artificial constraints. Backscatter is retrieved at the instrument's native resolution with an uncertainty between 2 and 20%. Extinction is less well constrained, retrieved at a resolution of 0.1–1 km depending on the quality of the data. The uncertainty in extinction is >15%, in part due to the consideration of short one-minute integrations, but is comparable to fair estimates of the error when using the standard Raman lidar technique. The retrieval is then applied to several hours of observation on 19 April 2010 of ash from the Eyjafjallajökull eruption. A highly depolarizing ash layer is found with a lidar ratio of 20–30 sr, much lower values than observed by previous studies. This potentially indicates a growth of the particles after 12–24 h within the planetary boundary layer. A lower concentration of ash within a residual layer exhibited a backscatter of 10 Mm−1 sr−1 and lidar ratio of 40 sr.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Reuse of Sludge from Pulp and Paper Industry Pilot and Full Scale Applications

1993 ◽  
Vol 28 (2) ◽  
pp. 17-26 ◽  
Author(s):  
V. Eroǧlu ◽  
A. M. Saatçi
Keyword(s):  
Economic Analysis ◽  
Paper Industry ◽  
Pulp And Paper Industry ◽  
Pulp And Paper ◽  
Full Scale ◽  
Primary Sludge ◽  
Recent Advances ◽  
Made In

Recent advances made in the reuse of pulp and paper industry sludges in hardboard production are explained. Data obtained from pilot and full-scale plants using primary sludge of a pulp and paper industry as an additive in the production of hardboard is presented. An economic analysis of the reuse of pulp and paper primary sludge in hardboard manufacturing is given. The quality of the hardboard produced is tested and compared with the qualities of the hardboard produced by the same plant before the addition of primary sludge. The hardboard with primary sludge additive has been used in Turkey for about a year in the manufacturing of office and home furniture. The results are very satisfactory when the primary sludge is used at 1/4 ratio.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

scholarly journals Determination of Environmental Factors for the Implementation of the Exploitability Index in Industrial Aggregate Mining Using Multi-Criteria Analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 196
Author(s):  
Julio Manuel de Luis-Ruiz ◽  
Benito Ramiro Salas-Menocal ◽  
Gema Fernández-Maroto ◽  
Rubén Pérez-Álvarez ◽  
Raúl Pereda-García
Keyword(s):  
Environmental Factors ◽  
Environmental Factor ◽  
Human Life ◽  
Accurate Estimation ◽  
Analytic Hierarchy ◽  
Main Components ◽  
Hierarchy Process ◽  
Made In

The quality of human life is linked to the exploitation of mining resources. The Exploitability Index (EI) assesses the actual possibilities to enable a mine according to several factors. The environment is one of the most constraining ones, but its analysis is made in a shallow way. This research is focused on its determination, according to a new preliminary methodology that sets the main components of the environmental impact related to the development of an exploitation of industrial minerals and its weighting according to the Analytic Hierarchy Process (AHP). It is applied to the case of the ophitic outcrops in Cantabria (Spain). Twelve components are proposed and weighted with the AHP and an algorithm that allows for assigning a normalized value for the environmental factor to each deposit. Geographic Information Systems (GISs) are applied, allowing us to map a large number of components of the environmental factors. This provides a much more accurate estimation of the environmental factor, with respect to reality, and improves the traditional methodology in a substantial way. It can be established as a methodology for mining spaces planning, but it is suitable for other contexts, and it raises developing the environmental analysis before selecting the outcrop to be exploited.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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