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Accurate forecasting of municipal solid waste (MSW) generation is critical for effective waste management, given the rising volumes of waste posing environmental and public health challenges. This study investigates the efficacy of hybrid forecasting models in predicting MSW generation trends across Organization for Economic Cooperation and Development (OECD) countries. The empirical analysis utilizes five distinct approaches – ARIMA, Theta model, neural networks, exponential smoothing state space (ETS), and TBATS models. MSW data spanning 1995–2021 for 29 OECD nations are analyzed using the hybrid models and benchmarked against individual ARIMA models. The results demonstrate superior predictive accuracy for the hybrid models across multiple error metrics, capturing complex data patterns and relationships missed by individual models. The forecasts project continued MSW generation growth in most countries but reveal nuanced country-level differences as well. The implications for waste management policies include implementing waste reduction and recycling programs, investing in infrastructure and technology, enhancing public education, implementing pricing incentives, rigorous monitoring and evaluation of practices, and multi-stakeholder collaboration. However, uncertainties related to model selection and data limitations warrant acknowledgment. Overall, this study affirms the value of hybrid forecasting models in providing robust insights to inform evidence-based waste management strategies and transition toward sustainability in the OECD region. [ABSTRACT FROM AUTHOR]

Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, such as temperature, precipitation, and river discharge. Using a comprehensive time series analysis, including Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models and correlation and regression analyses, we identify significant correlations between these climatic variables and flood events. Our findings indicate that rising temperatures (with a mean of 8.46 °C and a maximum of 9 °C) and increased precipitation (averaging 862.26 mm annually)are strongly associated with higher river discharge (mean 214.6 m3/s) and more frequent floods (mean 197.94 events per year). The ANN model outperformed the ARIMA model in flood forecasting, showing lower error metrics (e.g., RMSE of 10.86 vs. 18.83). The analysis underscores the critical impact of climate change on flood risks, highlighting the necessity of adaptive flood-management strategies that incorporate the latest climatic and socio-economic data. This research contributes to the understanding of flood dynamics in Germany and provides valuable insights into future flood risks. Combining flood management with groundwater recharge could effectively lower flood risks and enhance water resources' mitigation and management. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a growing public health concern. In this study, we focused on analyzing and forecasting the monthly number of accidents, number of injuries, and number of deaths in Algeria over the period (2015–2020). For this purpose, hybrid forecasting models based on equal weights and in-sample errors were fitted, and we compared them with the seasonal autoregressive moving average (SARIMA) models. The three models retained for forecasting until 2022 are all hybrid models, one based on equal weight and two models based on in-sample errors (using the RMSE indicator). Furthermore, the hybrid models outperformed the SARIMA models for short (6 months), medium (12 months), and long horizon (24 months). The forecasting results showed that we expect an increase in the number of accidents, the number of deaths, and the number of injuries over the next 12 months. Policymakers must enhance strategies for prevention and road safety, especially in rural areas, where the highest rate of fatalities is recorded. [ABSTRACT FROM AUTHOR]

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There are no studies and only limited data that compare the difference in mortality between twins and singletons in the Arab world. We studied the survival of 306,966 children, including 9,280 twins, over the period 1970-2013 in six Arab countries (Algeria, Egypt, Iraq, Mauritania, Sudan and Tunisia) based on the Multiple Indicator Cluster Survey (MICS) database. With the use of relative survival models, we estimated the mortality of twins relative to singletons by including socioeconomic and demographic variables. This study confirms the results of previous studies on the excess risk of death of twins compared to singletons. There is evidence that excess mortality decreases with follow-up; in addition, male twins have a higher risk of death compared to females for all countries except Tunisia. Wealth index and education levels of women are factors that influence the risk of mortality. It is recommended that these findings are considered when performing future health and population strategies in these Arab countries. [ABSTRACT FROM AUTHOR]