Improved accuracy of wildfire simulations using fuel hazard estimates based on environmental data

The 2019-2020 Australian forest fires are a harbinger of decreased prescribed burning effectiveness under rising extreme conditions

There is an imperative for fire agencies to quantify the potential for prescribed burning to mitigate risk to life, property and environmental values while facing changing climates. The 2019-2020 Black Summer fires in eastern Australia raised questions about the effectiveness of prescribed burning in mitigating risk under unprecedented fire conditions. We performed a simulation experiment to test the effects of different rates of prescribed burning treatment on risks posed by wildfire to life, property and infrastructure. In four forested case study landscapes, we found that the risks posed by wildfire were substantially higher under the fire weather conditions of the 2019-2020 season, compared to the full range of long-term historic weather conditions. For area burnt and house loss, the 2019-2020 conditions resulted in more than a doubling of residual risk across the four landscapes, regardless of treatment rate (mean increase of 230%, range 164-360%). Fire managers must prepare for a higher level of residual risk as climate change increases the likelihood of similar or even more dangerous fire seasons.

Study on a risk model for prediction and avoidance of unmanned environmental hazard

Comprehensive research is conducted on the design and control of the unmanned systems for electric vehicles. The environmental risk prediction and avoidance system is divided into the prediction part and the avoidance part. The prediction part is divided into environmental perception, environmental risk assessment, and risk prediction. In the avoidance part, according to the risk prediction results, a conservative driving strategy based on speed limit is adopted. Additionally, the core function is achieved through the target detection technology based on deep learning algorithm and the data conclusion based on deep learning method. Moreover, the location of bounding box is further optimized to improve the accuracy of SSD target detection method based on solving the problem of imbalanced sample categories. Software such as MATLAB and CarSim are applied in the system. Bleu-1 was 67.1, bleu-2 was 45.1, bleu-3 was 29.9 and bleu-4 was 21.1. Experiments were carried out on the database flickr30k by designing the algorithm. Bleu-1 was 72.3, bleu-2 was 51.8, bleu-3 was 37.1 and bleu-4 was 25.1. From the comparison results of the simulations of unmanned vehicles with or without a system, it can provide effective safety guarantee for unmanned driving.