An Effective Rainfall-Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points

With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash-Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.

Automated summary

This study used a LSTM neural network to build a ponding prediction model, with LSTM (msle) identified as the best model for accurately predicting the depth of ponding in the next 1 hour. LSTM (mae) showed better prediction performance when the ponding depth exceeded 30 mm.