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Deep neural network-based spatiotemporal heterogeneous data reconstruction for landslide detection

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Abstract

Landslides could cause huge threats to lives and cause property damages. In the landslide prediction system, environmental information can be collected through sensors to detect the possibility of landslide occurrences. However, the data collected by wireless sensor network systems (WSNs) may be lost due to sensor failures, external interferences, or other environmental factors, which may affect the accuracy of landslide predictions. In order to solve the problem of missing data, we propose a data reconstruction method based on rainfall intensity, soil moisture, slope, and slope direction and reconstruct missing data based on heterogeneous data and temporal and spatial relationships. A convolutional long short-term memory (ConvLSTM) deep neural network is trained to predict the missing time slot data. We use the predicted data to compensate for missing data. The results of the experiments show that the factor of safety of ConvLSTM achieves better RMSE in almost all of the missing data types and rates than LSTM. The mean and stdev forecast error of gradual fading with ConvLSTM at missing rate 30% are -0.001 and 0.033, respectively.

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Acknowledgements

This paper is an extended version of our paper entitled Reconstruction of deep neural network-based data for landslide detection published in the 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). This work is supported by the Ministry of Science and Technology, Taiwan, R.O.C., for financial support of this research under project numbers MOST 140-2221-E-194-064

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Authors and Affiliations

  1. Computer Engineering Department, Satya Wacana Christian University, Jl. Diponegoro 52-60, Salatiga, 50711, Central Java, Indonesia

    Darmawan Utomo

  2. Computer Science and Information Engineering, National Chung Cheng University, 168 University Road, Minhsiung, 62102, Chiayi, Republic of China

    Liang-Cheng Hu & Pao-Ann Hsiung

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  1. Darmawan Utomo
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  2. Liang-Cheng Hu
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Correspondence to Darmawan Utomo.

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Utomo, D., Hu, LC. & Hsiung, PA. Deep neural network-based spatiotemporal heterogeneous data reconstruction for landslide detection. Int J Data Sci Anal 17, 93–109 (2024). https://doi.org/10.1007/s41060-022-00358-5

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