The Poisson Regression Analysis for Occurrence of Floods

Cupal, Martin; Deev, Oleg; Linnertová, Dagmar

doi:10.1016/s2212-5671(15)00465-7

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…The Poisson regression model is a generalized linear model (GLM) that meets the classical assumptions with only one exception, the distribution. The dependent variable assumes the Poisson distribution [ 35 ]; regardless of whether the distribution is maintained or not, asymptotically normal and consistent estimators of

are obtained.…”

Section: Methodsmentioning

confidence: 99%

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Cordero-Ahiman

Vanegas

Franco-Crespo

et al. 2021

IJERPH

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Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.

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are obtained.…”

Section: Methodsmentioning

confidence: 99%

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Cordero-Ahiman

Vanegas

Franco-Crespo

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Poisson regression is a commonly used classical model, which was based upon the classical assumptions. It was further assumed that the dependent variable had Poisson distribution, as the random variable showed a value 0, 1, 2, 3, … n. It explained the distribution of y i or the expected value y i by the explanatory variables x i , as expressed below (Cupal et al 2015):…”

Section: Correlation Analysismentioning

confidence: 99%

Association between meteorological indicators and COVID-19 pandemic in Pakistan

Raza

Khan²,

Ali

et al. 2020

Environ Sci Pollut Res

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This study was designed to investigate the impact of meteorological indicators (temperature, rainfall, and humidity) on total COVID-19 cases in Pakistan, its provinces, and administrative units from March 10, 2020, to August 25, 2020. The correlation analysis showed that COVID-19 cases and temperature showed a positive correlation. It implies that the increase in COVID-19 cases was reported due to an increase in the temperature in Pakistan, its provinces, and administrative units. The generalized Poisson regression showed that the rise in the expected log count of COVID-19 cases was 0.024 times for a 1°C rise in the average temperature in Pakistan. Second, the correlation between rainfall and COVID-19 cases was negative in Pakistan. However, the regression coefficient between the expected log count of COVID-19 cases and rainfall was insignificant in Pakistan. Third, the correlation between humidity and the total COVID-19 cases was negative, which implies that the increase in humidity is beneficial to stop the transmission of COVID-19 in Pakistan, its provinces, and administrative units. The reduction in the expected log count of COVID-19 cases was 0.008 times for a 1% increase in the humidity per day in Pakistan. However, humidity and COVID-19 cases were positively correlated in Sindh province. It is required to create awareness among the general population, and the government should include the causes, symptoms, and precautions in the educational syllabus. Moreover, people should adopt the habit of hand wash, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to control the COVID-19.

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“…We conduct experiments by using the dataset of emergency calls of Ningbo (a 8 million population city of China). Our experimental results demonstrate remarkable improvements on forecasting performance in comparison with the results obtained by such existing methods as BPNN, LR and Poisson Regression models [8].…”

Section: Introductionmentioning

confidence: 60%

“…Poisson regression model [8] is a regression analysis model used for modeling technical data and association tables. Poisson regression assumes that the event rate λ is determined by a set of regressor variables, e.g., a 1 , a 2 , .…”

Section: B Poisson Regression Modelmentioning

confidence: 99%

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

et al. 2019

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Forecasting emergency calls are of great importance in practice. By forecasting the occurrence of unfortunate events, we can learn from these events and further prevent their occurrence in the future. However, because of the uncertainty of event occurrences, it is hard to guarantee their prediction accuracy. In this paper, a combined model, which consists of two parts, is proposed. The first part is a Poisson neural network model (PNN). It is responsible for basic forecasting, and its initial weights and thresholds are trained by applying a genetic algorithm. The second part consists of multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and multivariate gray (GM), which are responsible for estimating residual errors. The basic prediction result adjusted by the residual error is used as the final forecasting result. The proposed model fully takes the advantages of PNN, MLR, ARIMA, and GM, and thus improves forecasting performance. Our method has been applied to the emergency calls of Ningbo, China. The experimental results show that the proposed model has advantages over some existing forecasting models, such as a neural network model, Poisson regression, and stochastic configuration networks in terms of mean absolute percentage error. INDEX TERMS Accident forecasting, combined model, Poisson distribution, Poisson neural network, residual model.

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The Poisson Regression Analysis for Occurrence of Floods

Cited by 21 publications

References 11 publications

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Factors That Determine the Dietary Diversity Score in Rural Households: The Case of the Paute River Basin of Azuay Province, Ecuador

Association between meteorological indicators and COVID-19 pandemic in Pakistan

Forecasting Emergency Calls With a Poisson Neural Network-Based Assemble Model

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