Impact of Meteorological Parameters on COVID-19 Outbreak Using Machine Learning Techniques

2021 ◽  
Author(s):  
Arkya Bagchi ◽  
Anjum ◽  
Areeshaa Parveen ◽  
Rahul Katarya
Keyword(s):  
Machine Learning ◽  
Meteorological Parameters ◽  
Machine Learning Techniques ◽  
Learning Techniques

Nowcasting Lightning Occurrence Using Machine Learning Techniques: The Challenge of Identifying Outliers

2020 ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Meteorological Parameters ◽  
Warning System ◽  
Weather Forecast ◽  
Field Analysis ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Learning Techniques ◽  
Kennedy Space

<p>Lightning is formed in the atmosphere through the combination of complex dynamic and microphysical processes. Lightning can have a considerable influence on the environment and on the economy since it causes energy supply outages, forest fires, damages, injury and death of humans and livestock worldwide. Therefore, it is of great importance to be able to predict lightning incidence in order to protect people and installations. Despite numerous attempts to solve the important problem of lightning prediction (e.g., [1]–[3]), the complex processes and large number of parameters involved in the problem lend themselves to the potential application of a machine learning (ML) approach.</p><p>We recently proposed a ML-based lightning early-warning system with promising performance [4]. The proposed ML model is trained to nowcast lightning incidence during any one of  three consecutive 10-minute time intervals and within a circular area of 30 km radius around a meteorological station. The system uses the real-time measured values of four meteorological parameters that are relevant to the mechanisms of electric charge generation in thunderstorms, namely the air pressure at station level (QFE), the air temperature 2 m above ground, the relative humidity, and the wind speed. The proposed algorithm was implemented using the data from 12 meteorological stations in Switzerland between 2006-2017 with a granularity of ten minutes. The stations were selected to be well distributed among different ranges of altitude and terrain topographies.</p><p>The algorithm requires the filtering out of a portion of the data which are identified as outliers. However, the process of the automatic identification of outliers is a challenging task which could also affect the model’s performance. In this presentation, we discuss this problem and present approaches that can be used to optimize the process.</p><p> </p><p><strong>References</strong></p><p>[1]      D. Aranguren, J. Montanya, G. Solá, V. March, D. Romero, and H. Torres, “On the lightning hazard warning using electrostatic field: Analysis of summer thunderstorms in Spain,” J. Electrostat., vol. 67, no. 2–3, pp. 507–512, May 2009.</p><p>[2]      G. N. Seroka, R. E. Orville, and C. Schumacher, “Radar Nowcasting of Total Lightning over the Kennedy Space Center,” Weather Forecast., vol. 27, no. 1, pp. 189–204, Feb. 2012.</p><p>[3]      Q. Meng, W. Yao, and L. Xu, “Development of Lightning Nowcasting and Warning Technique and Its Application,” Adv. Meteorol., vol. 2019, pp. 1–9, Jan. 2019.</p><p>[4]      A. Mostajabi, D. L. Finney, M. Rubinstein, and F. Rachidi, “Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques,” npj Clim. Atmos. Sci., vol. 2, no. 1, p. 41, 2019.</p>

scholarly journals Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan L. Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Predictive Skill ◽  
Learning Techniques ◽  
Lightning Discharges

Abstract Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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