UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 2
February-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2302207


Registration ID:
508335

Page Number

c30-c38

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Title

EARLY WARNING SYSTEM FOR WEATHER PREDICTION: FOG AND CLOUD COMPATIBLE MODEL

Abstract

Disasters often led to economic, and human loses. Early predictions corresponding to disaster can allow administration to take preventive and precautionary measures Weathers are common and uncertain disasters that can occur due to disturbance in normal slope stability. Weathers often accompany earthquakes, rain, or eruptions. This research proposed an early warning system for Weather. Entire framework associated with proposed system consists of sensor, fog and cloud layer. Data acquisitions employed within sensor layer collects the data about the soil and land through sensors. Furthermore, pre-processing will be performed at sensor layer. Pre-processing mechanism remove any noise from the dataset. Fog layer contains feature reduction mechanism that is used to reduce the size of data to conserve energy of sensors during transmission of data. Furthermore, predictor variables selected within energy conservation mechanism will be used for exploratory data analysis (EDA). Main characteristics of data will be extracted using EDA. Furthermore, principal component analysis applied at fog layer analyses the dependencies between the attributes. Dependencies are calculated using correlation. Negatively skewed attributes will be rejected thus dimensionality of dataset is reduced further. All the gathered prime attributes are stored within cloud layer. K means clustering is applied to group the similar entities within same cluster. This step will reduce the overall execution time of prediction. Formed clusters are fed into ARIMA(Auto regressive integrated moving averages) for predictions. Relevant authorities can fetch the result by logging into the cloud. The effectiveness of proposed approach is proved at different levels using metrics such as classification accuracy and F-score.

Key Words

Fog computing, Weather prediction, energy efficiency, K means clustering, PCA, ARIMA

Cite This Article

"EARLY WARNING SYSTEM FOR WEATHER PREDICTION: FOG AND CLOUD COMPATIBLE MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.c30-c38, February-2023, Available :http://www.jetir.org/papers/JETIR2302207.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"EARLY WARNING SYSTEM FOR WEATHER PREDICTION: FOG AND CLOUD COMPATIBLE MODEL", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppc30-c38, February-2023, Available at : http://www.jetir.org/papers/JETIR2302207.pdf

Publication Details

Published Paper ID: JETIR2302207
Registration ID: 508335
Published In: Volume 10 | Issue 2 | Year February-2023
DOI (Digital Object Identifier):
Page No: c30-c38
Country: GURDASPUR, PUNJAB, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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