Related papers: Estimating Fire Weather Indices via Semantic Reaso…
The effectiveness and adequacy of natural hazard warnings hinges on the availability of data and its transformation into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be…
Wildfire forecasting problems usually rely on complex grid-based mathematical models, mostly involving Computational fluid dynamics(CFD) and Celluar Automata, but these methods have always been computationally expensive and difficult to…
This paper presents a novel approach in wildfire prediction through the integration of multisource spatiotemporal data, including satellite data, and the application of deep learning techniques. Specifically, we utilize an ensemble model…
Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without available reference…
Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables…
Increasing wildfire occurrence has spurred growing interest in wildfire spread prediction. However, even the most complex wildfire models diverge from observed progression during multi-day simulations, motivating need for data assimilation.…
Utilities in California conduct Public Safety Power Shut-offs (PSPSs) to eliminate the elevated chances of wildfire ignitions caused by power lines during extreme weather conditions. We propose Wildfire Risk Aware operation planning Problem…
Frequent and severe wildfires have been observed lately on a global scale. Wildfires not only threaten lives and properties, but also pose negative environmental impacts that transcend national boundaries (e.g., greenhouse gas emission and…
Environmental scientists frequently rely on time series of explanatory variables to explain their impact on an important response variable. However, sometimes, researchers are less interested in raw observations of an explanatory variable…
In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station…
The development of robust Early Warning Signals (EWS) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWS are statistical measures based on time series of climate state variables,…
Wildland fire smoke exposures are an increasing threat to public health, and thus there is a growing need for studying the effects of protective behaviors on reducing health outcomes. Emerging smartphone applications provide unprecedented…
As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation…
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distribution of the scatterers…
Estimation problems in wireless sensor networks typically involve gathering and processing data from distributed sensors to infer the state of an environment at the fusion center. However, not all measurements contribute significantly to…
In recent years wildfires have caused havoc across the world, especially aggravated in certain regions, due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on…
Weather foundation models (WFMs) have recently set new benchmarks in global forecast skill, yet their concrete value for the weather-sensitive infrastructure that powers modern society remains largely unexplored. In this study, we fine-tune…
Knowledge of Wi-Fi networks helps to guide future engineering and spectrum policy decisions. However, due to its unlicensed nature, the deployment of Wi-Fi Access Points is undocumented meaning researchers are left making educated guesses…
Recent research has demonstrated the potential of deep neural networks (DNNs) to accurately predict wildfire spread on a given day based upon high-dimensional explanatory data from a single preceding day, or from a time series of T…
Utilizing satellite imagery for wildfire detection presents substantial potential for practical applications. To advance the development of machine learning algorithms in this domain, our study introduces the \textit{Sen2Fire} dataset--a…