Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches

Melchior, Cristiane; Zanini, Roselaine Ruviaro; Guerra, Renata Rojas; Rockenbach, Dinei

doi:10.1016/j.ijforecast.2020.09.010

Cited by 28 publications

(12 citation statements)

References 42 publications

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“…A similar finding was noted by Kang and Ryu (2019), where they also found “original-cause material” as the second most influential attribute in predicting construction accident type, particularly related to scaffolding, stairways, ladder, edges and openings. These special works could be related to falls from height, which was also deemed as one of the main causes of occupational fatalities (Melchior et al. , 2020; Wu et al.…”

Section: Resultsmentioning

confidence: 99%

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

Koç

Ekmekçioğlu²,

Gürgün³

2022

ECAM

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PurposeCentral to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.Design/methodology/approachOccupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.FindingsThe results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.Practical implicationsThe proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.Social implicationsPractitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.Originality/valueThe literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.

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Section: Resultsmentioning

confidence: 99%

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

Koç

Ekmekçioğlu²,

Gürgün³

2022

ECAM

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“…The autoregressive moving average (ARMA) model [32] is a combination of the Auto Regressive (AR) and Moving Average (MA) models. Compared to the AR and MA models, it has higher accuracy, with two orders of p and q, known as the ARMA (p, q) model.…”

Section: Arima Modelmentioning

confidence: 99%

Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm

Li,

Zhou

2024

Mathematics

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With developments in global economic integration and the increase in future economic uncertainty, it is imperative to have the ability to predict future capital in relation to financial capital inflow and outflow predictions to ensure capital optimization is within a controllable range within the current macroeconomic environment and situation. This paper proposes an automated capital prediction strategy for the capital supply chain using time series analysis artificial intelligence methods. Firstly, to analyze the fluctuation and tail risk of the financial characteristics, the paper explores the financial characteristics for measuring the dynamic VaR from the perspectives of volatility, tail, and peak with the Bayesian peaks over threshold (POT) model. Following this, in order to make the modeling more refined, the forecast targets are split before modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) models and Prophet models. Finally, the time series modeling of the wavelet Long Short-Term Memory (LSTM) model is carried out using a two-part analysis method to determine the linear separated wavelet and non-linear embedded wavelet parts to predict strong volatility in financial capital. Taking the user capital flow of the Yu’e Bao platform, the results prove the feasibility and prediction accuracy of the innovative model proposed.

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“…Conforme o Anuário Estatístico da Previdência Social (AEPS), em 2021, foram concedidos pelo Instituto Nacional de Segurança Social (INSS) no Brasil cerca de 760,3 mil benefícios acidentários e apesar de apresentar uma redução 0,61% em relação a 2020, o país obteve um acréscimeto de 3,07 milhões de reais com desepesas com benefícios por acidente no trabalho, equivalente a um aumento de 3,16% (AEPS, 2021). Além disso, no ranque internacional, o Brasil ocupa o quarto lugar entre os países com maior número de acidentes de trabalho, atrás apenas da China, Índia e Indonésia (MELCHIOR et al, 2021). As lesões musculoesqueléticas relacionadas ao trabalho podem ser tratadas por meio de uma abordagem de demanda, que visa reduzir a carga física exigida dos trabalhadores e reduzir o risco de lesões.…”

Section: Introductionunclassified

Descriptive and temporal trend analysis of LER/DORT Cases in Brazil from 2012 to 2021

Oliveira

2023

CLIUM

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The Repetitive Strain Injury (RSI) or Work-related Musculoskeletal Disorders (WMSDs) are labor misfortunes that affect thousands of workers in Brazil, capable of causing irreversible sequelae, depending on the severity of the accident and the conditions in which the work is performed. In this context, this research aims to conduct a quantitative descriptive study to evaluate the most incident cases of work-related musculoskeletal disorders in Brazil from 2012 to 2021. The proposed method was based on a descriptive analysis along with a linear regression model to capture the trend of major occupational ICDs over ten years. The cases of work-related musculoskeletal disorders analyzed showed a reduction throughout the period, and the most common pathologies are upper limb injuries, especially back pain, which resulted in an average of 24.7 thousand accidents per year. Thus, it is recommended to adopt more effective preventive measures in order to reduce the statistics of accidents caused by work-related musculoskeletal disorders in the country.

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Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches

Cited by 28 publications

References 42 publications

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods

Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm

Descriptive and temporal trend analysis of LER/DORT Cases in Brazil from 2012 to 2021

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