Location Aware Climate Sensing and Real Time Data Analysis

Nallakaruppan, M. K.; Ilango, Harun Surej

doi:10.1109/wccct.2016.27

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Finally, a temporal prediction of the appearance of hotspots in the Rokan Hilir district, Riau province, Indonesia was produced by [9] using a Long Short Term Memory Recurrent Neural Network (LSTM RNN). This research was implemented using a model developed to predict the emergence of hotspots by considering the pattern of training data inferred from time-shaped data.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Kadir¹,

Dayana²,

Rosa³

et al. 2020

SAR Journal

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Various forms of disasters occur worldwide, one of which is fire. Indonesia has been suffering from frequent land and forest fires. These events are not a new phenomenon and seem to be an annual tradition, especially in the dry season. This nation was most affected by an excessively disastrous forest fire in 2015. The misfortunes suffered were massive and resulted in land and forest damage that may have great economic and environmental costs. One solution to reduce the impacts of such events is to predict the emergence of hotspots. Therefore, in this work, a modeling method using time series produced by the Box-Jenkins' Autoregressive Integrated Moving Average (ARIMA) model was used to predict the appearance of hotspots. Since the forecasting system does not expect any detailed form to be predicted in terms of the time series of historical data, the data demonstrated in the proposed model were different from data from other models used for prediction. The study was conducted based on monthly hotspot occurrence data from January 2014 through June 2019 in Riau Province, Indonesia. The data were downloaded from the collection of the "LAPAN-MODIS-Catalog". Based on the results shown, the Autoregressive Integrated Moving Average (ARIMA) model (2,1,2) produced good predictions based on its lowest value of Mean Squared Error (MSE), 9540.088. Moreover, the proposed model has produced highly accurate forecasts of hotspots for time periods of up to five months using the fitting model of ARIMA (2,1,2), and the values forecasted for 5 months ahead were 25, 31, 26, 30 and 27.

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Section: Related Workmentioning

confidence: 99%

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Kadir¹,

Dayana²,

Rosa³

et al. 2020

SAR Journal

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“…Based on concentration, the calculation of AQI takes place over a particular averaging period. AQI is a Piecewise Linear function which is also known as segmented function of the concentration of the pollutants.In terms of mathematics, concentration can be transformed to AQI as [24,25]:…”

Section: Introduction To Air Pollutionmentioning

confidence: 99%

The Dual Impact of Lockdown on Curbing COVID-19 Spread and Rise of Air Quality Index in India

Roy¹,

Chatterjee²

2021

The Impact of the COVID-19 Pandemic on Green Societies

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The number of reported cases in India has been scaling up in geometric progression despite the stringent lockdown norms imposed to keep people indoors since late March. Interestingly, on 31st March there were 1117 affected cases, 33,610 on 30th April and 511,478 till June 26-an unprecedented rise in the numbers. In the present research article, we propose a differential equation-based mathematical model for modeling India's COVID-19 that incorporates the lockdown effect while looking at the future predictions in terms of the spread and the extent to which lockdown has been effective in India. We have estimated the growth of COVID-19 across India using modified SIR modeling, which is a Compartmental model in Epidemiology. Further, the use of SIQR model to estimate the growth of this disease across the country. Also, a constant factor has been introduced in the model to measure the number of corona-affected patients count due to any accidental mass crowd gathering. Along with that, we analyse the pollution level of India under three conditional scenarios viz. Pre-Lockdown, During Lockdown and After Lockdown. From the epidemiological evidences, it is evident that several pollutants like pm 2.5, NO 2 , SO 2 , O 3 , CO noxious effects of pollution. Here we will analyse the basic contributing factor of pollution and which majorly impacts AQI. We will also visualise the change of AQI in the context of the season or a particular time, i.e. during the festive season and Diwali pollution highly increases and it continues till April. During COVID-19, to avoid the contamination and spreading of the virus, Govt of India declared Lockdown and due to this all the industrial works gets stopped and the reduction of vehicular waste also reduced and thus the concentration of pollutants (μg/m 3 ) decreases immensely. It can be interpreted that due to closure of industries and decreases in the number of vehicles, the concentration of the pollutants decreases thus it can be said that COVID-19 is a blessing to nature. But after reopening, i.e. unlock 1 the concentration, increases rapidly and immensely and from the reports, it is evident that in only in ecological regions, there is an increase of 400%. Thus after

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A Novel Approach for Analysis of Air Quality Index Before and After Covid-19 Using Machine Learning

Tiwari¹,

Pathak²,

Dey

2022

Lecture Notes in Electrical Engineering

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Location Aware Climate Sensing and Real Time Data Analysis

Cited by 10 publications

References 7 publications

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

Prediction of Hotspots in Riau Province, Indonesia Using the Autoregressive Integrated Moving Average (ARIMA) Model

The Dual Impact of Lockdown on Curbing COVID-19 Spread and Rise of Air Quality Index in India

A Novel Approach for Analysis of Air Quality Index Before and After Covid-19 Using Machine Learning

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