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首页> 外文会议>International Geoscience and Remote Sensing Symposium >Deep Neural Network-Based Data Reconstruction for Landslide Detection
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Deep Neural Network-Based Data Reconstruction for Landslide Detection

机译：基于深度神经网络的滑坡检测数据重建

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摘要

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 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 and soil moisture, which reconstructs missing data based on temporal relationships. It is based on the data trend in the past period of time. A Long Short-Term Memory (LSTM) deep neural network is trained to predict the data value in missing time slots. We use the predicted data to compensate for the missing data so as the elevate the accuracy not only of data, but also landslide predictions. Our method is compared with other reconstruction methods. The proposed LSTM model exhibit a smaller RMSE than the Linear Extrapolation (LE) method. Even if 90% of random data is lost, the RMSE results for the data reconstruction by LE and LSTM are, respectively, 0.033 and 0.036 for rainfall data and 0.029 and 0.032 for soil moisture data.
机译：山体滑坡可能导致巨大的威胁生命并导致财产损害。在滑坡预测系统中，可以通过传感器收集环境信息，以检测滑坡发生的可能性。然而，由于传感器故障，外部干扰或其他环境因素，所收集的数据可能会丢失，这可能会影响滑坡预测的准确性。为了解决数据缺失的问题，我们提出了一种基于降雨强度和土壤水分的数据重建方法，其基于时间关系重建缺失的数据。它基于过去的时间段的数据趋势。长期内记忆（LSTM）深神经网络训练，以预测缺失时隙中的数据值。我们使用预测的数据来补偿缺失的数据，以便不仅提升数据，而且还提升了数据，而是滑坡预测。我们的方法与其他重建方法进行比较。所提出的LSTM模型表现出比线性外推（LE）方法更小的RMSE。即使90％的随机数据丢失，LE和LSTM的数据重建的RMSE结果也分别为0.033和0.036，用于降雨数据和用于土壤水分数据的0.029和0.032。

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