Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

Ali, Arfan; Amin, Safaa; Ramadan, Hassan H.; Tolba, Mohamed F.

doi:10.1007/s00521-012-1178-9

Cited by 14 publications

(9 citation statements)

References 49 publications

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“…Satellite remote-sensing observations have been exploited to characterize aerosol layers and to assess parameterizations for regional and global models (e.g. Amiridis et al, 2010). Global networks of sun/sky radiometers, such as AErosol RObotic NEtwork (AERONET, Holben et al, 1998) measure the spectral aerosol optical depth (AOD) (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A neural network aerosol-typing algorithm based on lidar data

et al. 2018

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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.

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Section: Introductionmentioning

confidence: 99%

“…Roberts et al, 2010), satellite retrievals (e.g. Ali et al, 2012), multi-angle spectropolarimeters (e.g. Di Noia et al, 2015), nephelometers (e.g.…”

Section: Introductionmentioning

confidence: 99%

A neural network aerosol-typing algorithm based on lidar data

et al. 2018

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“…It is a self-organizing computational technique and it can solve many functions through pattern recognition (Shahriar and Nehdi 2011;Ahmed et al 2015). ANN can effectively be used to reconstruct nonlinear relationship learning from training (Ali et al 2013).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The main elements of an artificial neuron are indicated in Figure 1(b) which includes weights, bias and activation function (Azimzadegan et al 2012). A typical artificial neural network model has two parts; processing units (neurons) and connections between elements (Ali et al 2013), in which neurons are located in layers of network. A layered ANN structure, called multilayer perceptron (MLP), is one of the most widespread ANN methods especially in structural identification (He and Yan 2007) (see Figure 1(c)).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Recent Developments in Damage Identification of Structures Using Data Mining

Gordan

Razak

Ismail

et al. 2017

Lat. Am. j. solids struct.

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Civil structures are usually prone to damage during their service life and it leads them to loss their serviceability and safety. Thus, damage assessment can guarantee the integrity of structures. As a result, a structural damage detection approach including two main components, a set of accelerometers to record the response data and a data mining (DM) procedure, is widely used to extract the information on the structural health condition. In the last decades, DM has provided numerous solutions to structural health monitoring (SHM) problems as an all-inclusive technique due to its powerful computational ability. This paper presents the first attempt to illustrate the data mining techniques (DMTs) applications in SHM through an intensive review of those articles dealing with the use of DMTs aimed for classification-, prediction-and optimization-based data mining methods. According to this categorization, applications of DMTs with respect to SHM research area are classified and it is concluded that, applications of DMTs in the SHM domain have increasingly been implemented, in the last decade and the most popular techniques in the area were artificial neural network (ANN), principal component analysis (PCA) and genetic algorithm (GA), respectively.

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“…Environmental changes and global warming are among the most important challenges humans have faced in the last hundred years [1,2]. Earth system simulation shows that the current trend of greenhouse gas emissions may affect most ecosystems and the lives of over 3.5 billion people worldwide as early as 2050 [3].…”

Section: Introductionmentioning

confidence: 99%

A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

Nasrollahi

Razmi

Ghodsi

2018

PROMET

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Measuring carbon emissions is an essential step in taking required action to fight global warming. This research presents a computational method for measuring transport related carbon emissions in a healthcare supply network. The network configuration significantly impacts carbon emissions. First, a multi-objective mathematical programing model is developed for designing a healthcare supply network in the form of a two-graph location routing problem under demand and fuel consumption uncertainty. Objective functions are minimizing total cost and minimizing total fuel consumption. In the presented model, the demand of each customer must be completely satisfied in each time period, and backlog is not permitted. The number and capacity of vehicles are determined, and vehicles are heterogeneous. Furthermore, fuel consumption depends on traveling distance, vehicle and road conditions, and the load of a vehicle. The centroid method is applied to face demand uncertainty. Next, a multi-objective non-dominated ranked genetic algorithm (M-NRGA) is proposed to solve the model. Then, a Monte Carlo based approach is presented for measuring transport-related carbon emissions based on fuel consumption in supply network. Finally, the proposed approach is applied to the case of a healthcare supply network in the Fars province in Iran. The obtained results illustrate that the proposed approach is a practical tool in designing healthcare supply networks and measuring transport-related carbon emissions in the network.

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Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

Cited by 14 publications

References 49 publications

A neural network aerosol-typing algorithm based on lidar data

A neural network aerosol-typing algorithm based on lidar data

Recent Developments in Damage Identification of Structures Using Data Mining

A Computational Method for Measuring Transport Related Carbon Emissions in a Healthcare Supply Network under Mixed Uncertainty: An Empirical Study

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