Decision Support System Development of Wildland Fire: A Systematic Mapping

Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

Audio-Based Wildfire Detection on Embedded Systems

The occurrence of wildfires often results in significant fatalities. As wildfires are notorious for their high speed of spread, the ability to identify wildfire at its early stage is essential in quickly obtaining control of the fire and in reducing property loss and preventing loss of life. This work presents a machine learning wildfire detecting data pipeline that can be deployed on embedded systems in remote locations. The proposed data pipeline consists of three main steps: audio preprocessing, feature engineering, and classification. Experiments show that the proposed data pipeline is capable of detecting wildfire effectively with high precision and is capable of detecting wildfire sound over the forest’s background soundscape. When being deployed on a Raspberry Pi 4, the proposed data pipeline takes 66 milliseconds to process a 1 s sound clip. To the knowledge of the author, this is the first edge-computing implementation of an audio-based wildfire detection system.

Strategic Wildfire Response Decision Support and the Risk Management Assistance Program

In 2016, the USDA Forest Service, the largest wildfire management organization in the world, initiated the risk management assistance (RMA) program to improve the quality of strategic decision-making on its largest and most complex wildfire events. RMA was designed to facilitate a more formal risk management process, including the use of the best available science and emerging research tools, evaluation of alternative strategies, consideration of the likelihood of achieving objectives, and analysis of tradeoffs across a diverse range of incident objectives. RMA engaged personnel from a range of disciplines within the wildfire management system to co-produce actionable science that met the needs of the highly complex incident decision-making environment while aiming to align with best practices in risk assessment, structured decision-making, and technology transfer. Over the four years that RMA has been in practice, the content, structure, and method of information delivery have evolved. Furthermore, the RMA program’s application domain has expanded from merely large incident support to incorporate pre-event assessment and training, post-fire review, organizational change, and system improvement. In this article, we describe the history of the RMA program to date, provide some details and references to the tools delivered, and provide several illustrative examples of RMA in action. We conclude with a discussion of past and ongoing program adaptations and of how this can inform ongoing change efforts and offer thoughts on future directions.