Abstract
Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, namely a long short-term memory (LSTM), a gated recurrent unit (GRU), and a least-squares support vector machine (LSSVM), to forecast short-term streamflow. Based on three deterministic forecasts, the stochastic inflow scenarios were further developed using Bayesian model averaging (BMA) for quantifying uncertainty. The forecasting scheme was further coupled with a multi-reservoir optimization model, and the multi-objective programming was solved using the parameterized multi-objective robust decision-making (MORDM) approach. The AI-based management framework was applied and demonstrated over a multi-reservoir system (25 reservoirs) in the Zhoushan Islands, China. Three main conclusions were drawn from this study: (1) GRU and LSTM performed equally well on streamflow forecasts, and GRU might be the preferred method over LSTM, given that it had simpler structures and less modeling time; (2) higher forecast performance could lead to improved reservoir operation, while uncertain forecasts were more valuable than deterministic forecasts, regarding two performance metrics, i.e., water supply reliability and operating costs; (3) the relationship between the forecast horizon and reservoir operation was complex and depended on the operating configurations (forecast quality and uncertainty) and performance measures. This study reinforces the potential of an AI-based stochastic streamflow forecasting scheme to seek robust strategies under uncertainty.
Highlights
Multi-step streamflow forecast is of great importance for reservoir operations to determine optimal water allocations considering the current use and the carry-over storage for mitigating water scarcity risk in the future (Guo et al, 2018; Zhao et al, 2019)
Previous studies have identified that realtime reservoir operations are influenced by multiple uncertainties (Xu et al, 2020), among which inflow forecast uncertainty has been determined as the primary source, resulting in the risk of water shortage when the forecast inflow overestimates the actual inflow
This section gives a brief introduction to long short-term memory (LSTM), gated recurrent unit (GRU), and leastsquares support vector machine (LSSVM) models
Summary
Multi-step streamflow forecast is of great importance for reservoir operations to determine optimal water allocations considering the current use and the carry-over storage for mitigating water scarcity risk in the future (Guo et al, 2018; Zhao et al, 2019). Used ML approaches include artificial neural networks (ANNs) and least-squares support vector machines (LSSVMs) (Ghumman et al, 2018; Kisi et al, 2019; Meng et al, 2019; Adnan et al, 2020; Ali and Shahbaz, 2020). Such models have been proven to be efficient tools to model qualitative and quantitative hydrological variables and deal with nonlinear features in streamflow. Many applications that assessed them together are found in the hydrological field (Gao et al, 2020; Muhammad et al, 2019)
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