| Citation: | ZHAO Yu, CHEN Lixia, LIANG Mengjiao. Temporal probability prediction and meteorological early warning modeling of rainfall-induced landslide based on LSTM_TCN model[J]. Bulletin of Geological Science and Technology, 2024, 43(2): 201-214. doi: 10.19509/j.cnki.dzkq.tb20220657
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Incomplete landslide timing information can result in inaccuracies in the temporal relationship between landslides and rainfall, consequently affecting the precision of a critical rainfall threshold model.
To address this issue, this study focuses on rainfall-induced landslides in the Wanzhou District of Chongqing from 1995 to 2015. The Henghe Township, lacking historical landslide data, serves as the verification area. We proposed a prediction model for the daily temporal probability of landslide occurrence on a certain day based on long short-term memory (LSTM) and a temporal convolutional network (TCN). This method was used to reconstruct the temporal information of rainfall-induced landslide events by simulating the nonlinear relationship between the duration of landslides and rainfall. After the reconstruction of temporal information, the landslide events were verified and selected and subsequently applied to a reasonable division of the E-D effective rainfall threshold curve to establish the landslide meteorological warning model.
The results showed that the average temporal probability of rainfall-induced landslides predicted by the proposed method reached 90.33%, which was higher than that of the ANN (71.17%), LSTM (72.75%), and TCN (86.91%) models. Using temporal probabilities exceeding a 90% threshold, 18 data points, including 42 landslides in the verification area, are expanded to 201. Compared with using solely historical landslide events, the meteorological warning model based on expanded temporal information provides a more reasonable warning classification, and the effective warning rate at the severe warning level is increased by 42.86%.
This method can compensate for the shortage of landslide time information in field investigations and provide data support for early meteorological warning systems for rainfall-induced landslides, thus improving the accuracy of early meteorological warning systems.
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