As climate change intensifies extreme weather events, water disasters pose growing threats to global communities, making adaptive reservoir management critical for protecting vulnerable populations and ensuring water security. Modern water resource management faces unprecedented challenges from cascading uncertainties propagating through interconnected reservoir networks. These uncertainties, rooted in physical water transfer losses and environmental variability, make precise control difficult. For example, sending 10 tons downstream may yield only 8-12 tons due to evaporation and seepage. Traditional centralized optimization approaches suffer from exponential computational complexity and cannot effectively handle such real-world uncertainties, while existing multi-agent reinforcement learning (MARL) methods fail to achieve effective coordination under uncertainty. To address these challenges, we present MARLIN, a decentralized reservoir management framework inspired by starling murmurations intelligence. Integrating bio-inspired alignment, separation, and cohesion rules with MARL, MARLIN enables individual reservoirs to make local decisions while achieving emergent global coordination. In addition, a LLM provides real-time reward shaping signals, guiding agents to adapt to environmental changes and human-defined preferences. Experiments on USGS data show that MARLIN improves uncertainty handling by 23\%, cuts computation by 35\%, and accelerates flood response by 68\%, exhibiting super-linear coordination, with complexity scaling 5.4x from 400 to 10,000 nodes. These results demonstrate MARLIN's potential for disaster prevention and protecting communities through intelligent, scalable water resource management.

翻译：随着气候变化加剧极端天气事件，水灾害对全球社区构成的威胁日益增长，使得适应性水库管理对于保护脆弱人群和确保水安全至关重要。现代水资源管理面临着前所未有的挑战，这些挑战源于不确定性在相互连接的水库网络中传播引发的级联效应。这些不确定性根植于物理性输水损失和环境变化，使得精确控制变得困难。例如，向下游输送10吨水可能因蒸发和渗漏仅得到8-12吨。传统的集中式优化方法存在指数级计算复杂度，无法有效处理此类现实世界的不确定性，而现有的多智能体强化学习（MARL）方法则难以在不确定性下实现有效协调。为应对这些挑战，我们提出了MARLIN——一个受椋鸟群集智能启发的去中心化水库管理框架。MARLIN将生物启发的对齐、分离和内聚规则与MARL相结合，使单个水库能够做出局部决策，同时实现涌现的全局协调。此外，大型语言模型（LLM）提供实时奖励塑形信号，指导智能体适应环境变化和人为定义的偏好。基于USGS数据的实验表明，MARLIN将不确定性处理能力提升了23%，计算量减少了35%，并将洪水响应速度加快了68%，展现出超线性协调特性——其复杂度在节点数从400增至10,000时仅增加5.4倍。这些结果证明了MARLIN通过智能化、可扩展的水资源管理实现灾害预防和社区保护的潜力。