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Educational institutions play a significant role in the community spread of SARS-CoV-2 in Victoria. Despite a series of social restrictions and preventive measures in educational institutions implemented by the Victorian Government, confirmed cases among people under 20 years of age accounted for more than a quarter of the total infections in the state. In this study, we investigated the risk factors associated with COVID-19 infection within Victoria educational institutions using an incremental deep learning recurrent neural network-gated recurrent unit (RNN-GRU) model. The RNN-GRU model simulation was built based on three risk dimensions: (1) school-related risk factors, (2) student-related community risk factors, and (3) general population risk factors. Our data analysis showed that COVID-19 infection cases among people aged 10–19 years were higher than those aged 0–9 years in the Victorian region in 2020–2022. Within the three dimensions, a significant association was identified between school-initiated contact tracing (0.6110), vaccination policy for students and teachers (0.6100), testing policy (0.6109), and face covering (0.6071) and prevention of COVID-19 infection in educational settings. Furthermore, the study showed that different risk factors have varying degrees of effectiveness in preventing COVID-19 infection for the 0–9 and 10–19 age groups, such as state travel control (0.2743 vs. 0.3390), international travel control (0.2757 vs. 0.3357) and school closure (0.2738 vs. 0.3323), etc. More preventive support is suggested for the younger generation, especially for the 10–19 age group.

Over the last few decades, there has been an increase in the probability of occurrence of wildfires. Also known as bushfires, the catastrophe can be attributed to climate changes and extreme weather conditions. Australia’s dry and warm climate makes it prone to wildfires, which risks the ecosystem and decreases the forest area. Hence it is necessary to reduce bushfire risk by monitoring their intensity. The availability of remotely sensed data enables us to analyse wildfires, explore and discover patterns, and help provide real-time warnings. This paper examines the forest fire data from 2018-2020, considering parameters like Brightness (Prediction) and Fire Radiative Power (Classification). The analysis is conducted using several machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (kNN), eXtreme Gradient Boosting (xGB), Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), etc. The prediction models are evaluated using Mean squared error (MSE), Root Mean Square Error (RMSE), R squared (R2), and Mean Absolute Error (MAE). In contrast, the classification models are evaluated using accuracy, precision, recall, and F-1 score. Our study shows that the RF model is the best prediction model, and the ANN model is the best classification model compared to the baseline models.

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