scholarly journals Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

2021 ◽  
Vol 13 (5) ◽  
pp. 969
Author(s):  
Ka Lok Chan ◽  
Ehsan Khorsandi ◽  
Song Liu ◽  
Frank Baier ◽  
Pieter Valks
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Satellite Observations ◽  
Learning Approach ◽  
Annual Average ◽  
The Neural Network ◽  
Machine Learning Approach

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

Machine Learning Classification of Tree Cover Type and Application to Forest Management

2018 ◽  
Vol 9 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Duncan MacMichael ◽  
Dong Si
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Forest Management ◽  
Network Model ◽  
Neural Network Model ◽  
Nearest Neighbor ◽  
Tree Cover ◽  
K Nearest Neighbor ◽  
Cover Type ◽  
The Neural Network

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.

scholarly journals Sentiment Analysis using LSTM

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1338-1340
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Problem Statement ◽  
The Neural Network ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Area Of Concern

Extracting the sentiment of the text using machine learning techniques like LSTM is our area of concern. Classifying the movie reviews using LSTM is our problem statement. The reviews dataset is taken from the IMDB movie review dataset. Here we will classify a review based on the memory in the neural network of a LSTM cell state. Movie reviews often contain sensible content which describe the movie. We can manually decide whether a movie is good or bad by going through these reviews. Using machine learning approach we are classifying the movie reviews such that we can say that a movie is good or bad. LSTM is effective than many other techniques like RNN and CNN.

scholarly journals Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shipra Banik ◽  
A. F. M. Khodadad Khan ◽  
Mohammad Anwer
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Network Model ◽  
Rough Set ◽  
Neural Network Model ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
The Neural Network

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Machine Learning Classification of Tree Cover Type and Application to Forest Management

2020 ◽  
pp. 1141-1164
Author(s):  
Duncan MacMichael ◽  
Dong Si
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Forest Management ◽  
Network Model ◽  
Neural Network Model ◽  
Nearest Neighbor ◽  
Tree Cover ◽  
K Nearest Neighbor ◽  
Cover Type ◽  
The Neural Network

This article is driven by three goals. The first is to use machine learning to predict tree cover types, helping to address current challenges faced by U.S. forest management agencies. The second is to bring previous research in the area up-to-date, owing to a lack of development over time. The third is to improve on previous results with new data analysis, higher accuracy, and higher reliability. A Deep Neural Network (DNN) was constructed and compared with three baseline traditional machine learning models: Naïve Bayes, Decision Tree, and K-Nearest Neighbor (KNN). The neural network model achieved 91.55% accuracy while the best performing traditional classifier, K-Nearest Neighbor, managed 74.61%. In addition, the neural network model performed 20.97% better than the past neural networks, which illustrates both advances in machine learning algorithms, as well as improved accuracy high enough to apply practically to forest management issues. Using the techniques outlined in this article, agencies can cost-efficiently and quickly predict tree cover type and expedite natural resource inventorying.

scholarly journals Machine Learning Algorithms for Predicting Coronary Artery Disease: Efforts Toward an Open Source Solution

2020 ◽  
Author(s):  
Aravind Akella ◽  
Vibhor Kaushik
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Network Model ◽  
Neural Network Model ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Artery Disease

AbstractThe development of Coronary Artery Disease (CAD), one of the most prevalent diseases in the world, is heavily influenced by several modifiable risk factors. Predictive models built using machine learning (ML) algorithms may assist healthcare practitioners in timely detection of CAD, and ultimately, may improve outcomes. In this study, we have applied six different ML algorithms to predict the presence of CAD amongst patients listed in an openly available dataset provided by the University of California Irvine (UCI) Machine Learning Repository, named “the Cleveland dataset.” All six ML algorithms achieved accuracies greater than 80%, with the “Neural Network” algorithm achieving accuracy greater than 93%. The recall achieved with the “Neural Network” model is also highest of the six models (0.93). Additionally, five of the six algorithms resulted in very similar AUC-ROC curves. The AUC-ROC curve corresponding to the “Neural Network” algorithm is slightly steeper implying higher “true positive percentage” achieved with this model. We also extracted the variables of importance in the “Neural Network” model to help in the risk assessment. We have released the full computer code generated in this study in the public domain as a preliminary effort toward developing an open solution for predicting the presence of coronary artery disease in a given population and present a workflow model for implementing a possible solution.

A data-based machine learning approach for RPC time resolution study based on ToF reconstruction

2021 ◽  
Vol 16 (12) ◽  
pp. P12002
Author(s):  
X.Y. Xie ◽  
H.L. Xu ◽  
Q.Y. Li ◽  
Y.J. Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Performance Studies ◽  
Time Resolution ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Waveform Analysis ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach

Abstract A data-based machine learning approach is proposed to study the properties of time resolution of RPC detectors by measuring the time of flight of cosmic muons. This method utilises a multi-layer perceptron and a type of recurrent neural network called long short-term memory. The neural network is trained with the waveforms of RPC signals digitized by an oscilloscope at a sampling frequency of 10 GHz and a 2 GHz bandwidth. A data augmentation approach is implemented for labelling. Compared to the results from conventional waveform analysis, this approach achieves a better time resolution of 1-mm gap RPCs. Based on the data, the approach has a generalisation capacity for performance studies of other timing detectors.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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