Wildland fire decision support systems require accurate predictions of wildland fire spread. Fuel moisture content (FMC) is one of the important parameters controlling the rate of spread of wildland fire. However, dead FMC measurements are provided by a relatively sparse network of remote automatic weather stations (RAWS), while live FMC is relatively infrequently measured manually. We developed a high resolution, gridded, real-time FMC data sets that did not previously exist for assimilation into operational wildland fire prediction systems based on ML. We used surface observations of live and dead FMC to train machine learning models to estimate FMC based on satellite observations. Moderate Resolution Imaging Spectrometer Terra and Aqua reflectances are used to predict the live and dead FMC measured by the Wildland Fire Assessment System and RAWS). We evaluate multiple machine learning methods including multiple linear regression, random forests (RFs), gradient boosted regression and artificial neural networks. The models are trained to learn the relationships between the satellite reflectances, surface weather and soil moisture observations and FMC. After training on data corresponding to the temporally and spatially nearest grid points to the irregularly spaced surface FMC observations, the machine learning models could be applied to all grid cells for a gridded product over the Conterminous United States (CONUS). The results show generally that the rule-based approaches have the lowest errors likely due to the sharp decision boundaries among the predictors, and the RF approach that utilizes bagging to avoid over-fitting has the lowest error on the test dataset. The errors are typically between 25%−33% the typical variability of the FMC data, which indicate the skill of the RF in estimating the FMC based on satellite data and surface characteristics. The FMC gridded product based on the RF runs operationally daily over CONUS and can be assimilated into WRF-Fire for more accurate wildland fire spread predictions.

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.