Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Because of their fixed life and wide distribution, plants are the first victims of air pollution. The atmosphere is considered polluted when the increase of the rate of certain components causes harmful effects on the different constituents of the ecosystems. The study of the flow of air near a polluting source (cement plant in our case), allows to predict its impact on the surrounding plant ecosystem. Different factors are to be considered. The chemical composition of the air, the climatic conditions, and the impacted plant species are complex parameters to be analyzed using conventional mathematical methods. In this study, we propose a system based on artificial neural networks. Since artificial neural networks have the capacity to treat different complex parameters, their application in this domain is adequate. The proposed system makes it possible to match the input and output spaces. The variables that constitute the input space are the chemical composition, the concentration of the latter in the rainwater, their duration of deposition on the leaves and stems, the climatic conditions characterizing the environment, as well as the species of plant studied. The output variable expresses the rate of degradation of this species under the effect of pollution. Learning the system makes it possible to establish the transfer function and thus predict the impact of pollutants on the vegetation.

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Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

Biogeosciences ◽

10.5194/bg-14-4101-2017 ◽

2017 ◽

Vol 14 (18) ◽

pp. 4101-4124 ◽

Cited By ~ 37

Author(s):

Seyed Hamed Alemohammad ◽

Bin Fang ◽

Alexandra G. Konings ◽

Filipe Aires ◽

Julia K. Green ◽

...

Keyword(s):

Neural Networks ◽

Heat Flux ◽

Artificial Neural Networks ◽

Eddy Covariance ◽

Latent Heat ◽

Gross Primary Production ◽

Turbulent Fluxes ◽

Uncertainty Estimates ◽

Artificial Neural ◽

The Impact

Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1°  ×  1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

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Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence

10.5194/bg-2016-495 ◽

2016 ◽

Cited By ~ 5

Author(s):

Seyed Hamed Alemohammad ◽

Bin Fang ◽

Alexandra G. Konings ◽

Julia K. Green ◽

Jana Kolassa ◽

...

Keyword(s):

Neural Networks ◽

Heat Flux ◽

Artificial Neural Networks ◽

Latent Heat ◽

Gross Primary Production ◽

Sensible Heat Flux ◽

Turbulent Fluxes ◽

Input Dataset ◽

Artificial Neural ◽

The Impact

Abstract. A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn from the training datasets: a target input dataset is generated using three independent data sources and a triple collocation (TC) algorithm to define a prior distribution. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides surface turbulent fluxes from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are validated using FLUXNET tower measurements across various climates and conditions. WECANN performs well in most cases and is strongly constrained by SIF information. The impact of SIF on WECANN retrievals is evaluated by removing it from the input dataset of the ANN, and it shows that SIF has significant influence, especially in regions of high vegetation cover and in humid conditions. When compared to in situ eddy covariance observations, WECANN typically outperforms other estimates, particularly for sensible and latent heat fluxes.

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Approximation of the process of changes in deformation of land surface using artificial neural networks

E3S Web of Conferences ◽

10.1051/e3sconf/20185500009 ◽

2018 ◽

Vol 55 ◽

pp. 00009

Author(s):

Maria Mrówczyńska ◽

Jacek Sztubecki

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Land Surface ◽

Reference System ◽

Surface Deformation ◽

Copper Mining ◽

Vertical Displacements ◽

Artificial Neural ◽

The Stability ◽

The Impact

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.

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Experimental Implementation of a Neural Network Optical Channel Equalizer in Restricted Hardware Using Pruning and Quantization

10.21203/rs.3.rs-1236203/v1 ◽

2022 ◽

Author(s):

Diego Argüello Ron ◽

Pedro Jorge Freire De Carvalho Sourza ◽

Jaroslaw E. Prilepsky ◽

Morteza Kamalian-Kopae ◽

Antonio Napoli ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Computational Complexity ◽

Communication Systems ◽

Complexity Reduction ◽

Edge Computing ◽

Optical Channel ◽

Raspberry Pi ◽

Artificial Neural ◽

The Impact

Abstract The deployment of artificial neural networks-based optical channel equalizers on edge-computing devices is critically important for the next generation of optical communication systems. However, this is a highly challenging problem, mainly due to the computational complexity of the artificial neural networks (NNs) required for the efficient equalization of nonlinear optical channels with large memory. To implement the NN-based optical channel equalizer in hardware, a substantial complexity reduction is needed, while keeping an acceptable performance level. In this work, we address this problem by applying pruning and quantization techniques to an NN-based optical channel equalizer. We use an exemplary NN architecture, the multi-layer perceptron (MLP), and address its complexity reduction for the 30 GBd 1000 km transmission over a standard single-mode fiber. We demonstrate that it is feasible to reduce the equalizer’s memory by up to 87.12%, and its complexity by up to 91.5%, without noticeable performance degradation. In addition to this, we accurately define the computational complexity of a compressed NN-based equalizer in the digital signal processing (DSP) sense and examine the impact of using different CPU and GPU settings on power consumption and latency for the compressed equalizer. We also verify the developed technique experimentally, using two standard edge-computing hardware units: Raspberry Pi 4 and Nvidia Jetson Nano.

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Using Artificial Neural Networks to Forecast Market Response

Neural Networks in Business Forecasting ◽

10.4018/978-1-59140-176-6.ch002 ◽

2011 ◽

pp. 23-46

Author(s):

Leonard J. Parsons ◽

Ashutosh Dixit

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Sales Performance ◽

Marketing Mix ◽

Causal Modeling ◽

Market Response ◽

Model Based ◽

Artificial Neural ◽

Marketing Actions ◽

The Impact

Marketing managers must quantify the effects of marketing actions on contemporaneous and future sales performance. This chapter examines forecasting with artificial neural networks in the context of model-based planning and forecasting. The emphasis here is on causal modeling; that is, forecasting the impact of marketing mix variables, such as price and advertising, on sales.

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Impact Detection Using Artificial Neural Networks

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.488-489.767 ◽

2011 ◽

Vol 488-489 ◽

pp. 767-770 ◽

Cited By ~ 6

Author(s):

M. Ghajari ◽

Z. Sharif-Khodaei ◽

M.H. Aliabadi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Stiffened Panel ◽

Fe Method ◽

Impact Detection ◽

Impact Position ◽

Composite Stiffened Panel ◽

Location Detection ◽

Artificial Neural ◽

The Impact

In this work, a number of impacts on a composite stiffened panel fitted with piezoceramic sensors were simulated with the finite element (FE) method. During impacts, the contact force history and strains at the sensors were recorded. These data were used to train, validate and test two artificial neural networks (ANN) for the prediction of the impact position and the peak of the impact force. The performance of the network for location detection has been promising but the other network should be further improved to provide acceptable predictions about the peak force.

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Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks

International Journal of Pavement Engineering ◽

10.1080/10298436.2021.1938046 ◽

2021 ◽

pp. 1-13

Author(s):

Michael D. Elwardany ◽

Botros N. Hanna ◽

Mena Souliman

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Fatigue Life ◽

Artificial Neural ◽

The Impact

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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks

MATEC Web of Conferences ◽

10.1051/matecconf/201821904004 ◽

2018 ◽

Vol 219 ◽

pp. 04004 ◽

Cited By ~ 1

Author(s):

Anna Jakubczyk-Gałczyńska

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Residential Buildings ◽

High Reliability ◽

Residential Building ◽

Field Studies ◽

Good Tool ◽

Original Algorithm ◽

Artificial Neural ◽

The Impact

Traffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building with a high probability. The model to forecast the impact of vibrations on buildings is based on artificial neural networks [5]. The author’s own field studies carried out according to the Polish standard [6] and literature examples [7-10] have been used to create the algorithms. The results of the conducted analysis show that an artificial neural network can be considered a good tool to predict the impact of traffic–induced vibrations on residential buildings, with a sufficiently high reliability.

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