PREDICTING THE PERFORMANCE OF GREEN STORMWATER INFRASTRUCTURE USING MULTIVARIATE LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

The expected performance of Green Stormwater Infrastructure (GSI) is typically quantified through numerical models based on hydrologic parameters and physics-based equations. With numerical models, the choice of a spatio-temporal discretization scheme for the computational domain is a strenuous task that requires extensive calibration and potentially lab-based parameters and experimentation. The performance of GSI has high temporal dynamics due to natural, anthropogenic, and climatic processes that are not well represented by the traditional physics-based hydrologic models, which are calibrated against only a few historical observations and have a user-defined and constrained set of computational outcomes. Deep learning-based predictive models, such as Long Short-Term Memory (LSTM) neural networks, offer an exciting opportunity to quantify GSI performance, accounting for its highly dynamic and constantly evolving nature by leveraging advancements in observational data. A LSTM regression can overcome some of the limitations associated with traditional hydrological models to aid the development of a fully data-informed GSI performance predictor. To demonstrate the LSTM and traditional model outcomes, both methods were applied to a rain garden in Villanova, PA, USA. Specifically, a LSTM model was used to predict the recession of ponded water depth in the rain garden using five years of observed data.绿色雨水基础设施（Green Stormwater Infrastructure, GSI）的预期性能通常通过基于水文参数与物理过程方程的数值模型进行量化。在应用数值模型时，为计算域选择时空离散化方案是一项艰巨的任务，需要开展大量参数校准工作，且可能需要依托实验室测定的参数与实验数据。GSI的性能具有显著的时间动态特性，这是自然、人为与气候过程共同作用的结果，而传统基于物理原理的水文模型难以充分表征这些过程——这类模型仅依托少量历史观测数据进行校准，且其计算结果的集合由用户自定义并受限于预设范围。基于深度学习的预测模型，例如长短期记忆（Long Short-Term Memory, LSTM）神经网络，为量化GSI性能提供了极具前景的解决方案：通过利用观测数据的技术进展，能够兼顾GSI高度动态且持续演变的特性。LSTM回归模型可克服传统水文模型的部分局限，助力构建完全以数据为依据的GSI性能预测器。为对比验证LSTM模型与传统模型的效果，两种方法均被应用于美国宾夕法尼亚州维拉诺瓦的一处雨水花园。具体而言，研究人员利用五年的实地观测数据，通过LSTM模型预测该雨水花园内积水深度的消退过程。