A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework.

Wildfires are increasingly impacting the environment and human health. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. Lack of an adequate early warning system impacts the health and safety of vulnerable populations disproportionately and widens the inequality gap. In this project, a multi-modal wildfire prediction and early warning system has been developed based on a novel spatio-temporal machine learning architecture. A comprehensive wildfire database with over 37 million data points was created, including the historical wildfires, environmental and meteorological sensor data from the Environmental Protection Agency, and geological data. The data was augmented into 2.53 km × 2.53 km square grids to overcome the sensor network coverage limitations. Leading and trailing indicators for the wildfires are proposed, classified, and tested. The leading indicators are correlated to the risks of wildfire conception, whereas the trailing indicators are correlated to the byproducts of the wildfires. Additionally, geological data was incorporated to provide additional information for better assessment on wildfire risks and propagation. Next, a novel U-Convolutional Long Short-Term Memory (ULSTM) neural network was developed to extract key spatial and temporal features of the dataset, specifically to address the spatial nature of the location of the wildfire and time-progression temporal nature of the wildfire evolution. Through iterative improvements and optimization, the final ULSTM network architecture, trained with data from 2012 to 2017, achieved >97% accuracy for predicting wildfires in 2018, as compared to ∼76% using traditional Convolutional Neural Network (CNN) techniques. The final model was applied to conduct a retrospective study for the 2018-2022 wildfire seasons, and successfully predicted 85.7% of wildfires >300 K acres in size. This technique could enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives, protecting the environment, and avoiding economic damages.

Automating the Standardized Cosmesis and Health Nasal Outcomes Survey Classification with Convolutional Neural Networks.

Importance: Currently, the aesthetic appearance and structure of the nose in a rhinoplasty patient is evaluated by a surgeon, without automation. Objective: To compare the assessment of convolutional neural networks (CNNs) (machine learning) and a rhinoplasty surgeon's impression of the nose before rhinoplasty. Methods: Preoperative nasal images were scored using a modified standardized cosmesis and health nasal outcomes survey (SCHNOS) questionnaire. Artificial intelligence (AI) models based on CNNs were developed and trained to classify patient nasal aesthetics into one of five categories, representing even intervals on the SCHNOS scoring scale. The models' performances were benchmarked against expert surgeon evaluation. Results: Two hundred thirty-five preoperative patient images were included in the study. The best-performing AI model achieved 61% accuracy and 0.449 average Matthews Correlation Coefficient on new patients. Conclusions: This pilot study suggests a proof-of-concept for AI to allow an automated patient assessment tool trained on preoperative patient images with a potential utility for counseling rhinoplasty patients.