Citation: KC, U.; Aryal, J. Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering. Fire 2022, 5, 213. https://doi.org/10.3390/fire5060213 Academic Editor: Natasha Ribeiro Received: 14 October 2022 Accepted: 6 December 2022 Published: 9 December 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). fire Article Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering Ujjwal KC 1, * and Jagannath Aryal 2, * 1 CSIRO|Agriculture and Food, St Lucia, Brisbane, QLD 4067, Australia 2 Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, VIC 3053, Australia * Correspondence: ujjwal.kc@csiro.au (U.K.); jagannath.aryal@unimelb.edu.au (J.A.) Abstract: Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering. Keywords: wildfire; risk metric; risk characterization; clustering; data-driven approach 1. Introduction With an increased understanding of phenomena and advancements in computing technologies and observational sciences, natural disasters can be modeled and studied with more detail than ever before [1–4]. The precise information on the location of the disaster and the associated risks are in-demand elements in natural disaster modeling systems and the subsequent application of such systems in an operational environment. Thus, any spatial information on the disaster start locations and their propagation extent within the framework of risk quantification plays an inherent role in effective disaster management. Consequently, in the fire disaster space, fire authorities have started widely using operational fire simulations for making better-informed decisions for wildfire management. Under current state-of-the-art of wildfire management, fire practitioners and authorities quantify the risk associated with wildfires by running several fire simulations in a geograph- ical area by using operational fire spread models, collectively referred to as an ensemble and conducting statistical analyses [5–7]. These ensembles are computationally expensive but their recent extensive use for wildfire management has become possible due to un- precedented advancements in computing technologies such as cloud computing. The fire simulations, when aggregated on a massive scale, have created a unique opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running a single computationally expensive simulation, although the process is highly computationally intensive otherwise. Fire 2022, 5, 213. https://doi.org/10.3390/fire5060213 https://www.mdpi.com/journal/fire