scholarly journals An External Agribusiness Risk Analysis Using KBDI: A Case of Veldfires in the Northern Territory of Australia

2020 ◽  
Vol 3 (1) ◽  
pp. 37
Author(s):  
Toyi Maniki Diphagwe ◽  
Bernard Moeketsi Hlalele ◽  
Dibuseng Priscilla Mpakathi
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Crop Production ◽  
Time Series Data ◽  
Northern Territory ◽  
Return Level ◽  
Series Data ◽  
Burned Area ◽  
Trend Test ◽  
P Value

The 2019/20 Australian bushfires burned over 46 million acres of land, killed 34 people and left 3500 individuals homeless. Majority of deaths and buildings destroyed were in New South Wales, while the Northern Territory accounted for approximately 1/3 of the burned area. Many of the buildings that were lost were farm buildings, adding to the challenge of agricultural recovery that is already complex because of ash-covered farmland accompanied by historic levels of drought. The current research therefore aimed at characterising veldfire risk in the study area using Keetch-Byram Drought Index (KBDI). A 39-year-long time series data was obtained from an online NASA database. Both homogeneity and stationarity tests were deployed using a non-parametric Pettitt’s and Dicky-Fuller tests respectively for data quality checks. Major results revealed a non-significant two-tailed Mann Kendall trend test with a p-value = 0.789 > 0.05 significance level. A suitable probability distribution was fitted to the annual KBDI time series where both Kolmogorov-Smirnov and Chi-square tests revealed Gamma (1) as a suitably fitted probability distribution. Return level computation from the Gamma (1) distribution using XLSTAT computer software resulted in a cumulative 40-year return period of moderate to high fire risk potential. With this low probability and 40-year-long return level, the study found the area less prone to fire risks detrimental to animal and crop production. More agribusiness investments can safely be executed in the Northern Territory without high risk aversion.

scholarly journals STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 612
Author(s):  
Helin Yin ◽  
Dong Jin ◽  
Yeong Hyeon Gu ◽  
Chang Jin Park ◽  
Sang Keun Han ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Time Series Data ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Series Data ◽  
Percentage Error ◽  
Hot Pepper ◽  
Types Of Information

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

scholarly journals Time-series analysis of the risk factors for haemorrhagic fever with renal syndrome: comparison of statistical models

2006 ◽  
Vol 135 (2) ◽  
pp. 245-252 ◽  
Author(s):  
W. HU ◽  
K. MENGERSEN ◽  
P. BI ◽  
S. TONG
Keyword(s):  
Time Series ◽  
Water Level ◽  
Crop Production ◽  
Time Series Data ◽  
Linear Time ◽  
Additive Model ◽  
Series Data ◽  
Haemorrhagic Fever ◽  
Independent Variables ◽  
China Model

Three conventional regression models were compared using the time-series data of the occurrence of haemorrhagic fever with renal syndrome (HFRS) and several key climatic and occupational variables collected in low-lying land, Anhui Province, China. Model I was a linear time series with normally distributed residuals; model II was a generalized linear model with Poisson-distributed residuals and a log link; and model III was a generalized additive model with the same distributional features as model II. Model I was fitted using least squares whereas models II and III were fitted using maximum likelihood. The results show that the correlations between the HFRS incidence and the independent variables measured (i.e. difference in water level, autumn crop production and density of Apodemus agrarius) ranged from −0·40 to 0·89. The HFRS incidence was positively associated with density of A. agrarius and crop production, but was inversely associated with difference in water level. The residual analyses and the examination of the accuracy of the models indicate that model III may be the most suitable in the assessment of the relationship between the incidence of HFRS and the independent variables.

scholarly journals DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL

2020 ◽  
Vol 17 (36) ◽  
pp. 1186-1198
Author(s):  
Mustofa USMAN ◽  
N INDRYANI ◽  
WARSONO A. ◽  
AMANTO WAMILIANA
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Impulse Response ◽  
Time Series Data ◽  
Granger Causality Test ◽  
Share Price ◽  
Series Data ◽  
P Value ◽  
Test Statistics ◽  
Causality Test

The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.

A daily burned area mapping method using AVHRR time-series data

2021 ◽  
Author(s):  
Shuhan Lou ◽  
Yuanhong Liao ◽  
Yufu Liu ◽  
Yuqi Bai
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Time Series Data ◽  
Mapping Method ◽  
Series Data ◽  
Burned Area ◽  
Burnt Area ◽  
Area Data

<p>The study of complex interactions between fire and atmospheric dynamics of the earth system is drawing increasing attention in recent years, especially when fire seasons are extended due to global warming, where the historical daily burnt area data played a pivotal role in analyzing wildfire regimes change. Existing products could not fully meet the temporal requirements: daily burnt area data in global fire emissions database (GFED4) starts from mid-2000 using MODIS while ESA Fire Climate Change Initiative (FireCCILT10) Dataset from 1982 to 2017 is provided on a monthly grid.</p><p>Advanced Very High Resolution Radiometer (AVHRR) series of sensors are widely used to develop pre‐MODIS daily historical records. However, compared to MODIS, the AVHRR sensor has a lower radiometric and geometric quality and is missing Short Wave Infrared (SWIR) band. To address the data quality problem, this research study presents a time-series mapping method for daily burned area using AVHRR composite. Daily fire-sensitive indices are calculated to develop a time-series data composite which is masked by the burnable surface of GLASS_GLC land cover product. Then, Continuous Change Detection and Classification (CCDC) time-series model, which originally implemented on Landsat data monitoring land cover change, is revised to detect an abrupt change in the time-series data composite and remove noise, ensuring temporal consistency. The image of a time-series breakpoint is further classified using a spatial contextual method to distinguish biomass burning from other forest degradation change like a landslide and is used to generate burned area probability map.</p><p>The methodology is verified in California, US, where fuel aridity increased during 1984–2015 driven by anthropogenic climate change. The samples are collected based on the National Monitoring Trends in Burn Severity（MTBS）Burned Areas Boundaries Dataset from 1984 – 2018 and California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) fire perimeters from since 1950. Primary results show that the proposed method can effectively detect burned area on daily basis with CCDC algorithm reducing the complexity of change detection.</p>

scholarly journals Random Modeling of Daily Rainfall and Runoff Using a Seasonal Model and Wavelet Denoising

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Time Series Data ◽  
Daily Rainfall ◽  
Wavelet Denoising ◽  
Time Series Forecasting ◽  
Series Data ◽  
Perturbation Term ◽  
Seasonal Model ◽  
Runoff Modeling

Instead of Fourier smoothing, this study applied wavelet denoising to acquire the smooth seasonal mean and corresponding perturbation term from daily rainfall and runoff data in traditional seasonal models, which use seasonal means for hydrological time series forecasting. The denoised rainfall and runoff time series data were regarded as the smooth seasonal mean. The probability distribution of the percentage coefficients can be obtained from calibrated daily rainfall and runoff data. For validated daily rainfall and runoff data, percentage coefficients were randomly generated according to the probability distribution and the law of linear proportion. Multiplying the generated percentage coefficient by the smooth seasonal mean resulted in the corresponding perturbation term. Random modeling of daily rainfall and runoff can be obtained by adding the perturbation term to the smooth seasonal mean. To verify the accuracy of the proposed method, daily rainfall and runoff data for the Wu-Tu watershed were analyzed. The analytical results demonstrate that wavelet denoising enhances the precision of daily rainfall and runoff modeling of the seasonal model. In addition, the wavelet denoising technique proposed in this study can obtain the smooth seasonal mean of rainfall and runoff processes and is suitable for modeling actual daily rainfall and runoff processes.

scholarly journals Data Requirements for Model-Based Cancer Prognosis Prediction

2015 ◽  
Vol 14s5 ◽  
pp. CIN.S30801 ◽  
Author(s):  
Lori A. Dalton ◽  
Mohammadmahdi R. Yousefi
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Time Series Data ◽  
Population Data ◽  
Patient Data ◽  
Bayesian Regression ◽  
Cancer Prognosis ◽  
Small Samples ◽  
Series Data ◽  
Prognosis Prediction

Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated using optimal Bayesian classification, given patient data. It was assumed that (1) disease is the result of several mutations of a known healthy network and that these mutations and their probability distribution in the population are known and (2) only a single snapshot of the patient's gene activity profile is observed. It was shown that, even in ideal settings where cancer in the population and the effect of a drug are fully modeled, a single static measurement is typically not sufficient. Here, we study what measurements are sufficient to predict prognosis. In particular, we relax assumption (1) by addressing how population data may be used to estimate network probabilities, and extend assumption (2) to include static and time-series measurements of both population and patient data. Furthermore, we extend the prediction of prognosis classes to optimal Bayesian regression of prognosis metrics. Even when time-series data is preferable to infer a stochastic dynamical network, we show that static data can be superior for prognosis prediction when constrained to small samples. Furthermore, although population data is helpful, performance is not sensitive to inaccuracies in the estimated network probabilities.

The burned area mapping for Heilongjiang basin based on MODIS time series data

2015 ◽  
Vol 35 (17) ◽  
Author(s):  
杨伟 YANG Wei ◽  
张树文 ZHANG Shuwen ◽  
姜晓丽 JIANG Xiaoli
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Burned Area ◽  
Burned Area Mapping

A MODIS time series data based algorithm for mapping forest fire burned area

2013 ◽  
Vol 23 (3) ◽  
pp. 344-352 ◽  
Author(s):  
Wei Yang ◽  
Shuwen Zhang ◽  
Junmei Tang ◽  
Kun Bu ◽  
Jiuchun Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Fire ◽  
Time Series Data ◽  
Series Data ◽  
Burned Area

scholarly journals Does the Covid-19 Outbreak Impacts On Economic Growth? An Evidence from Indonesia

2021 ◽  
Vol 2 (3) ◽  
pp. 88-95
Author(s):  
Hilda Aprina ◽  
M. Sabri Abd. Majid ◽  
Vivi Silvia
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Cross Section ◽  
Direct Investment ◽  
Time Series Data ◽  
Regression Coefficient ◽  
Negative Impact ◽  
Percent Level ◽  
Series Data ◽  
P Value

This study aims to analyze the effectS of the COVID-19 pandemic, labor, domestic direct investment (DDI), AND foreign direct investment (FDI) on economic growth in Indonesia. The type of data used in this study is panel data, which is a combination of cross-section and THE time series data (Silvia, 2020). The cross-section data involves 34 provinces and time-series data covers the period from the first quarter of 2018 to the second quarter of 2021. The result found out that the regression coefficient of labor has a positive and significant effect at the 5 percent level, which means that if the number of workers increases by 1 percent, economic growth will increase by 0.03 percent. Furthermore, the FDI variable also has a significant and positive effect on economic growth in Indonesia. We can see in table 3.2 that the FDI variable is significant at the 5 percent level with a regression coefficient of 0.012, this means that an increase in FDI by 1 percent will accelerate economic growth by 0.012 percent. From the results of data processing obtained by the author, it can be seen that the DDI variable has a positive but not significant effect on economic growth in Indonesia, this can be seen from the p-value which is greater than 5 percent. The regression coefficient of -0.001 proves that the COVID-19 pandemic has a negative impact on economic growth in Indonesia. When the COVID-19 pandemic reached the territory of Indonesia, economic growth slowed by 0.001 percent.

scholarly journals Forecasting End of COVID – 19 in India Based on Time Series Analysis

2020 ◽  
Vol 5 (9) ◽  
pp. 763-772
Author(s):  
B S Kambo ◽  
Dr.Kulwinder Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
P Value ◽  
The Past ◽  
Lack Of Fit ◽  
Q Statistic ◽  
Past Trend

In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.

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