Flood forecasting in urban reservoir using hybrid recurrent neural network

Flood forecasting can provide accurate inferences and early warnings for flood control work during the flood season. Due to the variability of local rainfall and the complexity of geographic conditions, existing prediction methods were unable to accurately predict the flooding process in a particular basin. Additionally, the water level sensor generates a significant amount of noise in the inbound flow data during period measurement. To address these issues, this article proposes a real-time flood forecasting model, which is used to accurately predict flood trends and peak times in the flood period. The model uses a convolution kernel function to smooth out local noise and neighborhood values, minimizing the impact of non-stationary series on the training process while retaining the true evolution of the flood in the original data. In our model, we develop a time series attention mechanism that is used to apply various weights to time series input vectors, such as outflow flow and rainfall from upstream reservoirs, this mechanism also improves the accuracy of short-term series prediction. To obtain additional information about the output of the recurrent neural network layer, we also include a multivariate autoregressive integrated moving average module. This method can add a linear component to the output, allowing the prediction result to adapt to the input period's scale shift. This article develops matching models for interval and full basin floods based on the geographical characteristics of China's urban Reservoir and the river basin, thresholds are established based on the outflow from upstream reservoirs, which enables the flood forecasting system to dynamically adjust model parameters in response to the threshold, it also circumvents the scaling problem inherent in flood time series at various scales. We trained and predicted using 25 different types of floods in Ankang Reservoir from 2010 to 2020. Three on-site real-time forecasts of catastrophic flooding at the Ankang Reservoir were conducted in September 2021 to validate the model's accuracy. The algorithm's efficiency in forecasting flood inflows is demonstrated through comparisons to traditional hydrological models and other machine learning networks, and our model consistently forecasts the peak time and total flood volume with the least amount of error in the comparison algorithm.

Automated summary

This article proposes a real-time flood forecasting model that accurately predicts flood trends and peak times. The model uses a convolution kernel function to smooth local noise and incorporates a time series attention mechanism and a multivariate autoregressive integrated moving average module to improve prediction accuracy. By developing matching models based on geographical characteristics and dynamically adjusting model parameters, the scaling problem in flood time series is addressed. The model demonstrates high accuracy in real-time forecasts at Ankang Reservoir.