RESUMEN
Wildfires pose significant threats worldwide, requiring accurate prediction for mitigation. This study uses machine learning techniques to forecast wildfire severity in the Upper Colorado River basin. Datasets from 1984 to 2019 and key indicators like weather conditions and land use were employed. Random Forest outperformed Artificial Neural Network, achieving 72 % accuracy. Influential predictors include air temperature, vapor pressure deficit, NDVI, and fuel moisture. Solar radiation, SPEI, precipitation, and evapotranspiration also contribute significantly. Validation against actual severities from 2016 to 2019 showed mean prediction errors of 11.2 %, affirming the model's reliability. These results highlight the efficacy of machine learning in understanding wildfire severity, especially in vulnerable regions.
RESUMEN
Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations.