基于强化学习的非常规水资源优化配置模型

目前我国非常规水资源开发利用程度较低，水资源配置的系统不确定性较大，水资源优化配置模型有待提升。本文将强化学习方法引入水资源配置模型，以水资源的经济效益为优化目标，建立了基于强化学习方法的水资源优化配置模型，采用Python语言编制计算分析软件；以北京市水资源优化配置作为案例分析，将非常规水资源纳入配置体系，得到了研究区水资源优化配置方案及收益区间。结果表明，本文建立的基于强化学习的非常规水资源优化配置模型能合理分析不同来水情况总体经济效益的收益范围，与区间两阶段随机规划模型相比，能在一定程度上提高预期经济收益；不同来水年的收益情况表明非常规水资源对缓解北京市水资源短缺有重要作用。

Optimal allocation model of unconventional water resources based on reinforcement learning

Development and utilization degree of unconventional water resources in China are relatively low at present, and water resources allocation is complicated with a variety of uncertainties. Aimed at this problem, this paper applies the method of reinforcement learning to water resources allocation, and develops an optimal allocation model of Python coding with the objective function of maximizing the economic benefit. This model is applied in a case study of Beijing, taking the unconventional water resources into account, and focusing on comparison of different allocation schemes and their interval scales of economic profits. Results show it predicts the overall profits under different water inflow conditions satisfactorily, giving larger values than those predicted using the two-stage stochastic programming method. And utilizing the unconventional water resources would play a significant role in alleviating the city’s water shortage.