An edge intelligence empowered flooding process prediction using Internet of things in smart city

• Published in: Journal of parallel and distributed computing Vol. 165; pp. 66 - 78
• Main Authors: Chen, Chen, Jiang, Jiange, Zhou, Yang, Lv, Ning, Liang, Xiaoxu, Wan, Shaohua
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2022
• Subjects: Attention mechanism; Flood forecasting; GRU neural network; Internet of things; Smart city; R2; LSTM; A-BiLSTM; A-LSTM; A-BiGRU; GRU; RMSE; Flood forecasting; BiLSTM; Smart city; IoT; BiGRU; Attention mechanism; Internet of things; RNN; RE; A-GRU; GRU neural network; IDW
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2022.03.010

Floods result in substantial damage throughout the world every year. Accurate predictions of floods can significantly alleviate casualties and property losses. However, due to the complexity of hydrology process especially in a city with complicated pipe network, the accuracy of traditional flood forecasting models suffer from the performance degradation with the increasing of required prediction period. In the work, based on the collected historical data of Xixian City, Henan Province, China, using the Internet of Things system (IoT) in 2011-2018, a Bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism is proposed. In our model, the attention mechanism is used to automatically adjust the matching degree between the input features and output. Besides, we use a bidirectional GRU model, which can process the input sequence from two directions of time series (chronologically and antichronologically), then merge their representations together. Compared with the prediction model using Long Short Term Memory (LSTM), our method can generate better prediction result, as can be seen from the arrival time error and peak error of floods during multi-step predictions. •A BiGRU multi-step flood prediction model based on attention mechanism was proposed.•Model tests were designed and compared with relevant models based on LSTM neural network.•The results show that the model has smaller deterministic coefficient and RMSE.•Within the allowable error range, the prediction accuracy of our model can reach 100%.