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Wildfire damage analysis

Introduction

Wildfires are one of the biggest risks to ecosystems as well as to communities and their properties. The losses and impacts that result from them are enormous and can be catastrophic. But while this is the case, the use of ML and AI in assessing the extent of wildfire damage has grown in recent years, offering new ways of understanding the dynamics of these fires. This is an emerging area of the environmental industry, where more and more companies are now reaping the rewards of adopting new-age technologies such as AI.

Challenges

There are numerous challenges that one can encounter when attempting to assess wildfire damage and they are all quite complicated. The conventional approach tends to involve the use of on-site examinations which can be tedious and strenuous as well as posing risks to the assessors. Furthermore, these methods are often inadequate to establish the actual impact of a wildfire to the affected area especially when the area is hard to access. There is also a problem of condition volatility where damage may continue to grow or increase in severity before a proper assessment can be made. Also, there is a challenge of dealing with high variability of fire behaviors and impacts as a function of weather, vegetation and terrain, thus making it hard to come up with a robust and repeatable damage assessment approach.

AI Solutions

There are several ways that AI and ML can help address these issues. The first one is that machine learning algorithms can use satellite and drone data to local and assess the extent of the wildfire in real-time, even in the areas that are difficult to access. These algorithms can also model the future fire behaviour using past events and existing factors, which enables better decision making of the resources and plans. AI can also help to perform the damage assessment automatically, thus minimizing the number of people needed for the assessment and increasing the efficiency of the process. It is also important that AI is capable of learning different types of fire damage and its impact, which may help to give a better picture of the wildfire situation. Descartes Labs has employed the use of ML to develop fire prediction models that are able to pinpoint fire outbreaks prior to their occurrence.

Benefits

There are numerous advantages of applying AI in assessing wildfire damage. Firstly, it enhances the efficiency of the damage evaluation process and accuracy of the assessment reports that are used for the recovery and restoration processes and may thus lower the economic costs of wildfires. Secondly, it offers a safer way of conducting the traditional on the ground assessment thus minimizing on the risk to personnel. Third, AI can enable better preparation and allocation of resources to minimize the effects of fire disasters in the future. Lastly, the information derived from the AI analysis may be useful in the process of understanding the phenomena of wildfires and their consequences which in turn may result in enhanced prevention and management strategies. In California, Pacific Gas and Electric Company has employed AI to anticipate and avoid fires which has helped the company to minimize losses and expenses.

Return on Investment

In this case, the return on investment (ROI) of AI in wildfire damage analysis can be quite impressive. Improved and timely damage assessment can therefore help in minimizing the costs of the recovery and restoration processes as well as the overall economic effects of wildfires. The safety improvements have the potential of also resulting in cost savings since it may also help in minimizing the number of people required in the field and the risks that are involved. Furthermore, the predictive features of AI can help in avoiding future fires thereby saving a lot of costs in the long run. For instance, the Firebreak project by the Pacific Gas and Electric Company has used AI effectively to minimize the occurrence and intensity of fires, thereby cutting down on costs.

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