Highway landslides are a natural disaster that can have devastating impacts on transportation, the economy, society, and people's lives. To mitigate the damage caused by these landslides, it is crucial to assess their susceptibility. In this study, we propose a novel method for assessing the risk of highway landslides using an XGBoost model that utilizes a variety of data sources, including remote sensing data, terrain, and meteorology. The results of our research are highly promising. Our model achieved an AUC score greater than 0.93/0.83 on the test/validation set, indicating high accuracy in predicting the susceptibility of landslides. By identifying areas that are at high risk for landslides, we can take proactive steps to prevent or mitigate the damage caused by these natural disasters. The insights gained from our study have important implications for land use planning, infrastructure development, and emergency management decision-making.
KEYWORDS: Education and training, Neural networks, Global Positioning System, Interpolation, Statistical modeling, Performance modeling, Monte Carlo methods, Safety, Transportation, Roads
According to statistics, large heavy-haul truck is the main vehicle type causing serious traffic accidents in China and speeding is one of the most common cases. It is necessary to study the travel speed prediction of trucks and provide early warnings for drivers as well as manage departments in previous to serious traffic accidents. Based on the GPS generated by trucks driving in China, a simple two-stage neural network was proposed in this paper to predict the travel speed of trucks with Monte Carlo Dropout. The proposed network was composed of an LSTM Network and a Fully Connected Feedforward Neural Network. The MC Dropout was utilized as an optimum approach for improving the predictions. Accuracies of the proposed network were tested with real trajectories and the RMSE was about 1.61 km/h with a bias of around -0.57 km/h.
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