Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks
This work addresses a computational bottleneck for researchers and practitioners modeling extreme events like floods and heatwaves, though it is incremental as it improves efficiency rather than introducing a new paradigm.
The paper tackles the computational inefficiency of estimating parameters for the Generalized Extreme Value distribution using conventional maximum likelihood methods by proposing a neural network-based, likelihood-free approach, achieving comparable accuracy with significant computational speedup as demonstrated in simulations and applied to temperature data.
The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood-free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network-based method provides Generalized Extreme Value (GEV) distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000-year annual maximum temperature data from the Community Climate System Model version 3 (CCSM3) across North America for three atmospheric concentrations: 289 ppm $\mathrm{CO}_2$ (pre-industrial), 700 ppm $\mathrm{CO}_2$ (future conditions), and 1400 ppm $\mathrm{CO}_2$, and compare the results with those obtained using the maximum likelihood approach.