论文标题
基于对非平滑随机动力学系统的随机观察的河流环境恢复
River environmental restoration based on random observations of a non-smooth stochastic dynamical system
论文作者
论文摘要
土和土壤是河流环境必不可少的元素。大坝下游的环境和生态系统受到上游减少甚至停止沉积物供应的严重影响。从河外补充土壤和土壤已被认为是减轻此问题的有效方法。但是,尚未从理论方面考虑其具有成本效益的实施。本文提出了一个可处理的新随机控制模型,以解决此问题。河流环境中的沉积物动态遵循非平滑且连续的分段确定性动态。该模型假设对沉积物动力学的观察仅是随机和离散地进行的,并且可以在每个观察时间以成本来补充沉积物。这种部分观察假设与在应用中不断获得环境信息的不断获得的事实是一致的。惩罚沉积物耗竭的性能指数也具有非平滑术语。我们证明,这些非平滑性因素与动态编程原则协调,并以退化的椭圆形形式得出最佳方程,负责最具成本效益的沉积物补充政策。我们在简化条件下为折扣案例,千古案例和完整的信息案例在简化条件下进行分析并验证精确解决方案。使用高分辨率有限差方案来处理更现实的案例。然后,我们以数值方式提供最佳的沉积物补充政策。
Earth and soils are indispensable elements of river environment. Dam-downstream environment and ecosystems have been severely affected by reduced or even stopped sediment supply from the upstream. Replenishing earth and soils from outside the river has been considered as an effective way to mitigate this issue. However, its cost-effective implementation has not been considered from a theoretical side. This paper presents a tractable new stochastic control model to deal with this issue. The sediment dynamics in the river environment follow non-smooth and continuous-time piecewise deterministic dynamics. The model assumes that the observation of the sediment dynamics is carried out only randomly and discretely, and that the sediment can be replenished at each observation time with cost. This partial observation assumption is consistent with the fact that continuously obtaining the environmental information is difficult in applications. The performance index to penalize the sediment depletion has a non-smooth term as well. We demonstrate that these non-smoothness factors harmonize with a dynamic programming principle, and derive the optimality equation in a degenerate elliptic form governing the most cost-efficient sediment replenishment policy. We analytically derive and verify an exact solution under a simplified condition for a discounted case, an Ergodic case, and a complete information case. A more realistic case is handled using a high-resolution finite difference scheme. We then provide the optimal sediment replenishment policy numerically.