Reservoirs have been installed as long-term assets to guarantee water and energy security for decades, if not centuries. However, the effect of siltation undermines reservoirs' sustainability because it significantly reduces the reservoirs' original capacity. Extreme events such as typhoons, floods and droughts are posited to have extreme impacts on sediment inflow and deposition in reservoirs. The same holds true for ISMTs (implemented sediment management technologies), such as dredging, spilling and bypassing. However, the large-scale analysis of their effects on reservoir sedimentation progression, recovery and development was not feasible due to data scarcity and technological restrictions. The present paper closes this information gap by conducting a GRU (gated recurrent unit) neural network analysis of 1,224 Japanese reservoirs, for which the sedimentation, local precipitation, extreme events and ISMTs were monitored between 2000 and 2017. The network reveals the beneficial impacts of dredging, spilling and bypassing. The results also demonstrate the potential of smart management and improved monitoring for sedimentation threat abatement. Thus, foresighted engineering and dedicated governance action in flood and drought scenarios can significantly strengthen the sustainable behavior of key infrastructure elements such as reservoirs.

水库已作为长期资产安装，以保证几十年甚至几个世纪的水和能源安全。然而，淤积的影响破坏了水库的可持续性，因为它显着降低了水库的原始容量。假设台风、洪水和干旱等极端事件对水库的泥沙流入和沉积产生极端影响。ISMT（实施的沉积物管理技术）也是如此，例如疏浚、溢出和绕过。然而，由于数据稀缺和技术限制，对其对储层沉积进程、采收率和开发影响的大规模分析是不可行的。本文通过对 1,224 个日本水库进行 GRU（门控循环单元）神经网络分析，填补了这一信息空白，在 2000 年至 2017 年间监测了沉积、局部降水、极端事件和 ISMT。该网络揭示了疏浚、溢出和绕过的有益影响。结果还展示了智能管理和改进监测以减少沉积威胁的潜力。因此，在洪水和干旱情景中，有远见的工程和专门的治理行动可以显着加强水库等关键基础设施要素的可持续行为。