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Los Angeles wildfires were 10 times bigger than utility's AI forecast
June 9 (Reuters) - Southern California Edison's internal wildfire forecasts underestimated the potential size of the Eaton Canyon fire in Los Angeles by a factor of ten in the days leading up to a deadly conflagration in January, according to documents reviewed by Reuters. The miss suggests potential weaknesses in the utility's fire modeling capabilities that factored into its response to the January wildfire threats, despite being upgraded with improved computing, datasets and artificial intelligence. At the time, wildfires whipsawed through Los Angeles' western flank near Santa Monica and Eaton Canyon in the east as they consumed more than 34,000 acres (13,750 hectares) - or some 53 square miles - turning entire neighborhoods to ash. Although no official cause for the Eaton Canyon blaze has been released, numerous lawsuits have claimed SCE's decision to keep power flowing to some lines and towers in the Altadena area led to the circumstances that triggered it. SCE has said the cause and circumstances around the fire are under investigation and will be for some time, and defended its modeling capabilities. "We are confident with our fire spread modeling and weather forecasting," Raymond Fugere, SCE's asset intelligence director, told Reuters in an interview. Fugere said SCE's simulations could have shown higher estimates for acres burned in hard-hit areas. Variations in wind patterns and available fuels in hard-hit areas may not have been fully accounted for in the fire spread modeling, he said. "But overall, we do feel confident with our modeling because it is giving us very actionable information to be able to make decisions," he said. SCE's simulations predicted a Jan. 7 ignition in Eaton Canyon that could scorch about 1,000 acres within eight hours without fire suppression, according to an SCE fire potential forecast obtained by Reuters through a public records request. SCE told Reuters those fire spread simulations were factored into the utility's power shutoff decisions as strong seasonal winds and dry conditions escalated the looming wildfire threat. The Eaton fire ignited as forecast on Jan. 7, but ultimately consumed some 14,000 acres, destroying around 9,400 homes and buildings, and killing 17 civilians - making it the centerpiece of one of the costliest natural disasters in U.S. history. Joseph Mitchell, a scientist and wildfire expert witness for California utility regulators, said SCE's predictions missed the mark mainly because its models were only running simulations that extend eight hours after an ignition. The bulk of the Eaton fire's damage happened well after the eight-hour mark. Michael Wara, a wildfire policy expert at Stanford Law School, said the wildfire modeling may also have erred because it is better tuned to simulating fire in dense shrubs and woodlands, instead of blocks of homes and businesses. "Altadena was a wildland fire for about 20 minutes, and then it became an urban conflagration ... where houses are burning houses down, and where gardens are the fuel type not ... mixed conifer forests," Wara said. SCE, a unit of Edison International (EIX.N), opens new tab, acknowledged it is evaluating changes to its wildfire risk models, including whether to use 24-hour fire spread simulations in the future. "The January 2025 wildfires raise important questions regarding the spread of wildfires into built urban environments," the company said in a May 16 filing with regulators. SCE has previously acknowledged that 24-hour simulations might capture more extreme events where firefighting resources are limited, according to regulatory filings with the California Public Utilities Commission prior to the fires. But SCE's Fugere said the longer simulations produce more uncertainty. UPGRADED MODELS SCE's forecast was the biggest test yet of upgraded forecasting capabilities since California Governor Gavin Newsom launched the "Wildfire Innovation Sprint" in 2019 - an initiative to encourage the use of AI to predict disasters and safeguard lives and property. Since then, SCE has built four supercomputer clusters capable of generating 13 billion simulations across 400 weather scenarios and 29 million ignition points, according to regulatory filings. The company also began using the services of Technosylva, a La Jolla, California-based company, which received $383,000 in state funding in 2019 to develop forecasting tools for utilities and emergency responders. Technosylva Chief Executive Bryan Spear told Reuters his company's risk models captured the magnitude of the Los Angeles wildfire five days in advance, allowing firefighters to make better preparations for the looming catastrophe. SCE's equipment has not been blamed for starting the massive Palisades fire, but the utility's forecast also vastly underestimated its potential size. The separate blaze started on the same day as the Eaton Canyon fire. SCE's forecast projected up to 1,000 acres burned in the Pacific Palisades area, according to the document. Actual wildfire destruction there included 23,448 acres burned, 12 civilian deaths and nearly 7,000 structures destroyed, according to Cal Fire. Together the Eaton and Palisades fires destroyed more than 16,000 structures and caused most of the $250 billion in economic losses estimated by AccuWeather. SCE plans to spend another $8 million on upgrading fire science and modeling this year, up from $2 million in 2018, company disclosures show. Reporting by Tim McLaughlin, editing by Deepa Babington Our Standards: The Thomson Reuters Trust Principles., opens new tab Suggested Topics:Environment
[2]
Los Angeles Wildfires Were 10 Times Bigger Than Utility's AI Forecast
(Reuters) -Southern California Edison's internal wildfire forecasts underestimated the potential size of the Eaton Canyon fire in Los Angeles by a factor of ten in the days leading up to a deadly conflagration in January, according to documents reviewed by Reuters. The miss suggests potential weaknesses in the utility's fire modeling capabilities that factored into its response to the January wildfire threats, despite being upgraded with improved computing, datasets and artificial intelligence. At the time, wildfires whipsawed through Los Angeles' western flank near Santa Monica and Eaton Canyon in the east as they consumed more than 34,000 acres (13,750 hectares) - or some 53 square miles - turning entire neighborhoods to ash. Although no official cause for the Eaton Canyon blaze has been released, numerous lawsuits have claimed SCE's decision to keep power flowing to some lines and towers in the Altadena area led to the circumstances that triggered it. SCE has said the cause and circumstances around the fire are under investigation and will be for some time, and defended its modeling capabilities. "We are confident with our fire spread modeling and weather forecasting," Raymond Fugere, SCE's asset intelligence director, told Reuters in an interview. Fugere said SCE's simulations could have shown higher estimates for acres burned in hard-hit areas. Variations in wind patterns and available fuels in hard-hit areas may not have been fully accounted for in the fire spread modeling, he said. "But overall, we do feel confident with our modeling because it is giving us very actionable information to be able to make decisions," he said. SCE's simulations predicted a Jan. 7 ignition in Eaton Canyon that could scorch about 1,000 acres within eight hours without fire suppression, according to an SCE fire potential forecast obtained by Reuters through a public records request. SCE told Reuters those fire spread simulations were factored into the utility's power shutoff decisions as strong seasonal winds and dry conditions escalated the looming wildfire threat. The Eaton fire ignited as forecast on Jan. 7, but ultimately consumed some 14,000 acres, destroying around 9,400 homes and buildings, and killing 17 civilians - making it the centerpiece of one of the costliest natural disasters in U.S. history. Joseph Mitchell, a scientist and wildfire expert witness for California utility regulators, said SCE's predictions missed the mark mainly because its models were only running simulations that extend eight hours after an ignition. The bulk of the Eaton fire's damage happened well after the eight-hour mark. Michael Wara, a wildfire policy expert at Stanford Law School, said the wildfire modeling may also have erred because it is better tuned to simulating fire in dense shrubs and woodlands, instead of blocks of homes and businesses. "Altadena was a wildland fire for about 20 minutes, and then it became an urban conflagration ... where houses are burning houses down, and where gardens are the fuel type not ... mixed conifer forests," Wara said. SCE, a unit of Edison International, acknowledged it is evaluating changes to its wildfire risk models, including whether to use 24-hour fire spread simulations in the future. "The January 2025 wildfires raise important questions regarding the spread of wildfires into built urban environments," the company said in a May 16 filing with regulators. SCE has previously acknowledged that 24-hour simulations might capture more extreme events where firefighting resources are limited, according to regulatory filings with the California Public Utilities Commission prior to the fires. But SCE's Fugere said the longer simulations produce more uncertainty. UPGRADED MODELS SCE's forecast was the biggest test yet of upgraded forecasting capabilities since California Governor Gavin Newsom launched the "Wildfire Innovation Sprint" in 2019 - an initiative to encourage the use of AI to predict disasters and safeguard lives and property. Since then, SCE has built four supercomputer clusters capable of generating 13 billion simulations across 400 weather scenarios and 29 million ignition points, according to regulatory filings. The company also began using the services of Technosylva, a La Jolla, California-based company, which received $383,000 in state funding in 2019 to develop forecasting tools for utilities and emergency responders. Technosylva Chief Executive Bryan Spear told Reuters his company's risk models captured the magnitude of the Los Angeles wildfire five days in advance, allowing firefighters to make better preparations for the looming catastrophe. SCE's equipment has not been blamed for starting the massive Palisades fire, but the utility's forecast also vastly underestimated its potential size. The separate blaze started on the same day as the Eaton Canyon fire. SCE's forecast projected up to 1,000 acres burned in the Pacific Palisades area, according to the document. Actual wildfire destruction there included 23,448 acres burned, 12 civilian deaths and nearly 7,000 structures destroyed, according to Cal Fire. Together the Eaton and Palisades fires destroyed more than 16,000 structures and caused most of the $250 billion in economic losses estimated by AccuWeather. SCE plans to spend another $8 million on upgrading fire science and modeling this year, up from $2 million in 2018, company disclosures show. (Reporting by Tim McLaughlin, editing by Deepa Babington)
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Southern California Edison's AI-powered wildfire forecasts significantly underestimated the scale of recent Los Angeles fires, raising questions about the reliability of AI in disaster prediction and management.
In a stark revelation of the limitations of current artificial intelligence systems in disaster prediction, Southern California Edison's (SCE) internal wildfire forecasts dramatically underestimated the potential size of the Eaton Canyon fire in Los Angeles. The AI-powered models predicted a burn area just one-tenth the size of the actual devastation that occurred in January 2025 12.
Source: Reuters
The Eaton Canyon fire, which ignited on January 7, 2025, as forecasted, ultimately consumed approximately 14,000 acres, destroying around 9,400 homes and buildings, and tragically claiming 17 civilian lives. This catastrophe formed the centerpiece of one of the costliest natural disasters in U.S. history 12.
Simultaneously, another blaze in the Pacific Palisades area wreaked havoc, burning 23,448 acres, causing 12 civilian deaths, and destroying nearly 7,000 structures. Together, the Eaton and Palisades fires obliterated more than 16,000 structures and contributed to an estimated $250 billion in economic losses 12.
SCE's simulations, despite recent upgrades, predicted that a potential ignition in Eaton Canyon would scorch only about 1,000 acres within eight hours without fire suppression. This forecast fell drastically short of the actual outcome 12.
Experts point to several factors contributing to this significant miscalculation:
Limited Simulation Time: Joseph Mitchell, a wildfire expert witness for California utility regulators, noted that SCE's models only ran simulations extending eight hours after ignition, while the bulk of the Eaton fire's damage occurred beyond this timeframe 12.
Urban Fire Spread: Michael Wara, a wildfire policy expert at Stanford Law School, suggested that the models may be better tuned to simulating fires in dense shrubs and woodlands, rather than urban environments where houses and gardens become the primary fuel 12.
SCE's forecast was a major test of upgraded forecasting capabilities implemented after California Governor Gavin Newsom's "Wildfire Innovation Sprint" initiative in 2019. The utility has invested heavily in AI-powered disaster prediction:
Despite these advancements, the January 2025 wildfires exposed significant gaps in the AI models' predictive capabilities. SCE has acknowledged the need for improvement and is evaluating changes to its wildfire risk models, including the possibility of implementing 24-hour fire spread simulations 12.
This incident raises critical questions about the reliability of AI in disaster prediction and management. While AI has shown promise in various fields, its application in complex, real-world scenarios like wildfire prediction still faces significant challenges.
SCE plans to invest an additional $8 million in upgrading fire science and modeling this year, a substantial increase from the $2 million spent in 2018 12. This increased investment underscores the urgent need for more accurate and comprehensive AI models in disaster forecasting.
As climate change continues to exacerbate the frequency and intensity of wildfires, the development of more robust AI prediction systems becomes increasingly crucial. The lessons learned from this catastrophic event will likely drive further research and innovation in the field of AI-powered disaster management.
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