Agronomic-meteorological model for weather forecasting to predict the rainfall using machine learning techniques

2016 ◽  
Vol 2 (2) ◽  
pp. 183
Author(s):  
Baghavathi Priya Sankaralingam ◽  
Usha Sarangapani
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Machine Learning Techniques ◽  
Meteorological Model ◽  
Learning Techniques

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Weather Forecasting using Machine Learning Techniques

2019 ◽  
Author(s):  
Siddharth Singh ◽  
Mayank Kaushik ◽  
Ambuj Gupta ◽  
Anil Kumar Malviya
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals An Investigation of Weather Forecasting using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 72-78
Author(s):  
Mrs. N.Vanitha ◽  
J.Haritha
Keyword(s):  
Machine Learning ◽  
Material Science ◽  
High Performance ◽  
Weather Forecasting ◽  
Machine Learning Techniques ◽  
Distant Future ◽  
Climate Conditions ◽  
Learning Techniques ◽  
Complex Models ◽  
Performance Computing

Customarily, climate expectations are performed with the assistance of enormous complex models of material science, which use distinctive air conditions throughout a significant stretch of time. In this paper, we studied  a climate expectation  strategy that uses recorded information from  numerous climate stations to prepare basic AI models, which can give usable figures about certain climate conditions for the not so distant future inside a brief  timeframe These conditions are frequently flimsy on account of annoyances of the climate framework, making the models give mistaken estimates.[1] The model are for the most part run on many hubs in an enormous High Performance Computing (HPC) climate which burns through a lot of energy.. The modes can be run on significantly less asset serious conditions. In this paper we describe that the sufficient to be utilized status of the workmanship methods. Moreover, we described that it is valuable to use the climate stations information from various adjoining territories over the information of just the region for which climate anticipating is being performed.

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
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