A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

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Coseismic and postseismic deformation estimation of the 2011 Tohoku earthquake in Kanto Region, Japan, using InSAR time series analysis and GPS

Remote Sensing of Environment ◽

10.1016/j.rse.2015.07.016 ◽

2015 ◽

Vol 168 ◽

pp. 374-387 ◽

Cited By ~ 4

Author(s):

Tamer ElGharbawi ◽

Masayuki Tamura

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Tohoku Earthquake ◽

2011 Tohoku Earthquake ◽

Postseismic Deformation ◽

The 2011 Tohoku Earthquake ◽

Series Analysis ◽

Kanto Region

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Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

International Journal for Research in Applied Sciences and Biotechnology ◽

10.31033/ijrasb.8.6.8 ◽

2021 ◽

Vol 8 (6) ◽

pp. 38-46

Author(s):

Mohammad Karim Ahmadzai

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Moving Average ◽

Arima Model ◽

Wheat Crop ◽

Series Analysis ◽

The People ◽

Self Sufficiency ◽

Univariate Time Series ◽

Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).

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Data Analysis Time Series For Forecasting The Greenhouse Effect

ZERO Jurnal Sains Matematika dan Terapan ◽

10.30829/zero.v3i2.7914 ◽

2019 ◽

Vol 3 (2) ◽

pp. 86

Author(s):

Fauzah Umami ◽

Hendra Cipta ◽

Ismail Husein

Keyword(s):

Time Series ◽

Data Analysis ◽

Time Series Analysis ◽

Greenhouse Effect ◽

Reference Data ◽

Moving Average ◽

Arima Model ◽

Mean Square ◽

Autoregressive Integrated Moving Average ◽

The Earth

<span lang="EN-US">The greenhouse effect is a term used to describe the earth having a greenhouse effect where the sun's heat is trapped by the earth's atmosphere. This study aims to model the greenhouse effect and then predict the greenhouse effect in the coming period using the Autoregressive Integrated Moving Average (ARIMA) method. In this case, time series analysis and reference data for 31 months are used, from the period January 2017 - July 2019, the results of the ARIMA model that are suitable for forecasting the greenhouse effect are ARIMA (4.2.0) with Mean Square Error (MSE) of 161885</span>

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A stochastic model for time series prediction of the number of post-earthquake fire ignitions in buildings based on the ignition record for the 2011 Tohoku Earthquake

Earthquake Spectra ◽

10.1177/8755293019878184 ◽

2019 ◽

Vol 36 (1) ◽

pp. 232-249 ◽

Cited By ~ 1

Author(s):

Tomoaki Nishino ◽

Akihiko Hokugo

Keyword(s):

Time Series ◽

Stochastic Model ◽

Time Series Prediction ◽

Tohoku Earthquake ◽

Fire Risk ◽

Seismic Intensity ◽

2011 Tohoku Earthquake ◽

The 2011 Tohoku Earthquake ◽

Fire Ignitions ◽

Electrical Supply

This article presents the development of a stochastic model for time series prediction of the number of post-earthquake fire ignitions in buildings for use in post-earthquake fire risk assessment. Two kinds of Poisson regression models with an explanatory variable of JMA instrumental seismic intensity were applied to 126 ignitions affected by ground motion, which were extracted from the ignition record for the 2011 Tohoku Earthquake: (1) a time-dependent occurrence model for the ignitions from electricity-related sources, which is coupled with a statistical model for electrical supply rate after an earthquake, and (2) a time-independent occurrence model for the ignitions from gas-related sources, oil-related sources, and others. In order to verify the models, time series prediction of the number of ignitions in the 2011 Tohoku Earthquake was conducted using Monte Carlo simulation. From the calculated results, we concluded that the models could reasonably explain the occurrence tendency of ignitions in the 2011 Tohoku Earthquake.

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A Multiplicative Seasonal Arima Model for Nigerian Unemployment Rates

The Bulletin of Society for Mathematical Services and Standards ◽

10.18052/www.scipress.com/bsmass.3.46 ◽

2012 ◽

Vol 3 ◽

pp. 46-53

Author(s):

Ette Harrison Etuk

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Moving Average ◽

Arima Model ◽

Positive Trend ◽

Seasonal Differences ◽

Autoregressive Integrated Moving Average ◽

Unemployment Rates ◽

Seasonal Model ◽

Time Plot

Time series analysis of Nigerian Unemployment Rates is done. The data used is monthly from 1948 to 2008. The time plot reveals a slightly positive trend with no clear seasonality. A multiplicative seasonal model is suggestive given seasonality that typically tends to increase with time. Seasonal differencing once produced a series with no trend nor discernible stationarity. A non-seasonal differencing of the seasonal differences yielded a series with no trend but with a correlogram revealing stationarity of order 12, a nonseasonal autoregressive component of order 3 and a seasonal moving average component of order 1. A multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (3, 1, 0)x(0, 1, 1)12, is fitted to the series. It has been shown to be adequate.

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