In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.
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http://dx.doi.org/10.1007/s11356-024-33335-5 | DOI Listing |
Heliyon
January 2025
Institute of Basic Operational Technology, China Telecom Research Institute, Guangzhou, 510630, China.
Accurate and efficient traffic prediction directly determines the construction scale and investment budget of communication networks, which is crucial for network planning. Despite the rise of popular machine learning models, traditional statistical models maintain significant advantages in interpretability, controllability and simplicity, retaining an essential role in contemporary communication network traffic prediction. This paper analyzes and predicts the inter-provincial egress traffic of 31 provinces in a large-scale operational IP backbone network using traditional regression analysis, the time series Prophet model, and a novel combination of these two prediction models.
View Article and Find Full Text PDFJMIR Form Res
December 2024
School of Media and Journalism, Kent State University, Kent, OH, United States.
Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Immunity, Quzhou Center for Disease Control and Prevention, Quzhou, Zhejiang, China.
Background: HFMD is a common infectious disease that is prevalent worldwide. In many provinces in China, there have been outbreaks and epidemics of whooping cough, posing a threat to public health.
Purpose: It is crucial to grasp the epidemiological characteristics of HFMD in Quzhou and establish a prediction model for HFMD to lay the foundation for early warning of HFMD.
J Surg Res
December 2024
Department of Pediatric Surgery, SSM Health Cardinal Glennon Children's Hospital, St. Louis, Missouri; Department of Pediatric Surgery, St. Louis University, St. Louis, Missouri.
Introduction: Rising pediatric firearm-related fatalities in the United States strain Trauma Centers. Predicting trauma volume could improve resource management and preparedness, particularly if daily forecasts are achievable. The aim of the study is to evaluate various machine learning models' accuracy on monthly, weekly, and daily data.
View Article and Find Full Text PDFHealth Care Sci
December 2024
School of Computer Science and Engineering, Vellore Institute of Technology Vellore India.
Background: The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.
Methods: The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data.
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