An integrated approach for weather forecasting and disaster prediction using deep learning architecture based on memory Augmented Neural Network’s (MANN’s)

2021 ◽  
Author(s):  
P.M. Satwik ◽  
Meenatchi Sundram
Keyword(s):  
Deep Learning ◽  
Weather Forecasting ◽  
Integrated Approach ◽  
Disaster Prediction

scholarly journals Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning

2021 ◽  
Author(s):  
Aryaman Sinha ◽  
Mayuna Gupta ◽  
K S S Sai Srujan ◽  
Hariprasad Kodamana ◽  
Sandeep Sukumaran
Keyword(s):  
Deep Learning ◽  
Sea Level ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Learning Model ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Deep Learning Model

<div><div><div><p>The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.</p></div></div></div>

scholarly journals Weather Nowcasting Using Deep Learning Techniques

2021 ◽  
Author(s):  
Makhamisa Senekane ◽  
Mhlambululi Mafu ◽  
Molibeli Benedict Taele
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Weather Forecasting ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Precipitation Forecasting ◽  
Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

scholarly journals Logistic Regression for Employability Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 2471-2478
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Weather Forecasting ◽  
Previous Knowledge ◽  
Recruitment Process ◽  
Proposed Model ◽  
Unknown Event ◽  
The Right

Prediction is a conjecture about something which may happen. Prediction need not be based upon the previous knowledge or experience on the unknown event of interest in the future. But it is a necessity for mankind to foresee and make the right decisions to live better. Every person does predictions but the quality of the predictions differs and that differentiates successful persons and unsuccessful persons. In order to automate the prediction process and to make quality predictions available to every person, machines are trained to make predictions and such field comes under machine learning and later on deep learning algorithms. Various fields such as health care, weather forecasting, natural calamities, and crime prediction are some of the applications of prediction. The researchers have applied the field of prediction to see whether a model can predict the employability of a candidate in a recruitment process. Organizations use human expertise to identify a skilled candidate for employment based on various factors and now these organizations are trying to migrate to automated systems by harnessing the benefits of the exponential growth in the area of machine learning and deep learning. This investigation presents the development of a model to predict the employability by using Logistic Regression. A set of candidates was tested in the proposed model and results are discussed in this paper.

Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning

2021 ◽  
pp. 56-66
Author(s):  
Angela Maria Vinod ◽  
Dharathi Venkatesh ◽  
Dishti Kundra ◽  
N. Jayapandian
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Disaster ◽  
Disaster Prediction

scholarly journals Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 684 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Numerical Model ◽  
Wind Velocity ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Global Analysis ◽  
Wrf Model ◽  
Weather Data ◽  
Northern Taiwan

A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.

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