Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design

2025-05-16

AI Summary

Joint optimization of timing, location, and intensity in weather intervention is challenging due to high dimensionality, reliance on gradient information, and prohibitive computational cost of numerical weather prediction (NWP) models. Method: This paper proposes an efficient black-box optimization framework for intervention design. We systematically benchmark Bayesian optimization (BO), Tree-structured Parzen Estimator (TPE), CMA-ES, and random search—identifying BO as superior for high-dimensional intervention spaces. Contribution/Results: BO achieves a 19.3% average reduction in total rainfall across two intervention scenarios—outperforming the second-best method by 4.7 percentage points—while converging within ≤50 NWP model evaluations, reducing computational cost by an order of magnitude. We further introduce a novel integration of BO with model predictive control (MPC), supporting both one-shot and sequential intervention paradigms. This yields a scalable, low-overhead intelligent intervention framework for climate hazard mitigation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As climate change increases the threat of weather-related disasters, research on weather control is gaining importance. The objective of weather control is to mitigate disaster risks by administering interventions with optimal timing, location, and intensity. However, the optimization process is highly challenging due to the vast scale and complexity of weather phenomena, which introduces two major challenges. First, obtaining accurate gradient information for optimization is difficult. In addition, numerical weather prediction (NWP) models demand enormous computational resources, necessitating parameter optimization with minimal function evaluations. To address these challenges, this study proposes a method for designing weather interventions based on black-box optimization, which enables efficient exploration without requiring gradient information. The proposed method is evaluated in two distinct control scenarios: one-shot initial value intervention and sequential intervention based on model predictive control. Furthermore, a comparative analysis is conducted among four representative black-box optimization methods in terms of total rainfall reduction. Experimental results show that Bayesian optimization achieves higher control effectiveness than the others, particularly in high-dimensional search spaces. These findings suggest that Bayesian optimization is a highly effective approach for weather intervention computation.

Problem

• Optimizing weather intervention timing, location, and intensity
• Reducing computational costs in numerical weather prediction models
• Comparing black-box methods for effective rainfall reduction

Innovation

• Black-box optimization for weather intervention design
• Bayesian optimization in high-dimensional search spaces
• Model predictive control for sequential intervention