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Lag-aware LSTM forecasting of 5-minute stormwater inflow in a sponge city community: a cross-correlation-driven feature framework
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DOI：10.3389/frwa.2026.1893590.png)
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Abstract
En 中文
Community-scale sponge city retention tanks need accurate short-term inflow forecasts. The forecasts support automated pump scheduling and overflow prevention. Most existing data-driven models were built for natural catchments or city-scale combined sewers. Community-scale sites differ. They show persistent dry-weather baseflow and compound detention from distributed low-impact development (LID) facilities. These features limit how well existing frameworks transfer. This study builds a lightweight; lag-aware framework for 5 min-ahead inflow prediction. A cross-correlation function (CCF) analysis extracts one physically interpretable rain-fall–inflow lag. The lag is combined with autoregressive inflow terms; recent rainfall; and multi-scale cumulative rainfall to form a 12-feature input. The framework was tested on 100 days of 5-min rainfall and inflow records from a 51484.6 m2 LID-equipped residential community in City P; China. The site includes permeable pavements; depressed green spaces; and bioretention cells. The CCF identified a representative rainfall–inflow response timescale of about 70 min; consistent with the compound detention behavior of the LID facilities. Five models were compared under the same features. A dual-layer LSTM reached the highest test-set NSE of 0.7436. A single-layer LSTM (0.7403); SVR (0.7398); and dual-layer GRU (0.7348) fell within a 1% NSE band. A Random Forest baseline reached only 0.6724. The NSE ceiling reflects the baseflow-dominated variance of the inflow series; not a weakness of the models. The ablation study shows that the CCF-derived lag feature gives the largest single rainfall-related accuracy gain. Forecasts are near-unbiased at the 5 min step. The compact feature space lets operators swap the model to match on-site computing limits; which makes the framework easy to deploy at other sponge city communities.
Keywords:
LSTM
sponge city
cross-correlation analysis
real-time control
low-impact development
stormwater inflow forecasting
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