Applied Sciences, Vol. 12, Pages 12334: An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points

Figure 1. LSTM structure flow chart.

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Figure 2. Model framework.

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Figure 3. Predicted results of water depth at each time-step (al) are the prediction results of 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 35 min, 40 min, 45 min, 50 min, 55 min and 60 min.

Figure 3. Predicted results of water depth at each time-step (al) are the prediction results of 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 35 min, 40 min, 45 min, 50 min, 55 min and 60 min.

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Figure 4. Evaluation of prediction accuracy at each moment measured by (a) RMSE, (b) MAE, (c) MAPE and (d) NSE.

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Table 1. Model accuracy comparison.

Table 1. Model accuracy comparison.

TimeLSTM (msle) vs. LSTM (mse)LSTM (msle) vs. LSTM (mae)LSTM (mse) vs. LSTM (mae)RMSEMAEMAPE (%)NSERMSEMAEMAPE (%)NSERMSEMAEMAPE (%)NSE5 min−1.92−1.43−18.910.43−0.67−0.84−15.900.131.250.593.01−0.3010 min−1.37−1.10−13.640.31−0.32−0.46−5.990.071.040.647.64−0.2515 min−1.26−1.06−13.360.29−1.19−0.85−14.110.270.060.20−0.75−0.0220 min−1.28−0.87−10.950.30−0.42−0.55−2.130.090.860.328.82−0.2125 min0.120.01−4.03−0.031.030.393.50−0.250.910.387.52−0.2230 min−0.97−0.66−12.180.23−1.09−0.94−8.840.26−0.12−0.283.340.035–30 min
average−1.11−0.85−12.180.25−0.45−0.54−7.250.100.670.314.93−0.1635 min−0.35−0.53−15.210.09−0.17−0.34−5.240.050.180.199.97−0.0540 min−0.74−0.62−14.200.19−1.10−0.92−10.410.29−0.35−0.303.790.1045 min−0.48−0.41−14.180.13−0.21−0.57−8.430.060.27−0.165.74−0.0750 min−0.76−0.70−17.790.22−1.15−1.07−12.170.34−0.39−0.375.620.1255 min−0.51−0.21−2.920.15−1.79−1.26−7.370.58−1.28−1.05−4.450.4360 min−0.82−0.79−20.460.24−1.49−1.27−19.460.47−0.67−0.481.000.2235–60 min
average−0.61−0.54−14.130.17−0.99−0.91−10.510.30−0.38−0.363.610.12full time average−0.86−0.70−13.150.21−0.72−0.72−8.880.200.15−0.034.27−0.02

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