An intelligent landslide monitoring device and method based on an LSTM long short-term memory network

Abstract

The invention discloses an intelligent landslide monitoring device and method based on an LSTM long and short term memory network, and relates to the field of landslide monitoring early warning and artificial intelligence. The method comprises the following steps of obtaining different input characteristic values of landslide historical monitoring data, such as surface deformation, deep displacement, underground water level, pore water pressure, seepage flow velocity and landslide instability, adding a data label, and dividing the monitoring data into a training set, a verification set and a test set according to a certain proportion; carrying out gradient descent training on the training set under an LSTM network to obtain a training model; using the verification set and the test set operate in the training model to obtain an accuracy result, determining the model and the data set adjustment direction through result analysis, and obtaining a monitoring model with high accuracy throughcontinuous debugging. By combining an artificial intelligence optimization algorithm, the accuracy of landslide disaster prediction is improved, the cost of the landslide disaster monitoring device is reduced, and the landslide disaster monitoring device is popularized to monitoring of other types of landslides in a large range after being combined with transfer learning.

Description

technical field [0001] The invention relates to the field of artificial intelligence deep learning in geological disaster landslide prediction and early warning, in particular to an intelligent landslide monitoring device and method based on LSTM long-short-term memory network. Background technique [0002] Because landslide disasters will bring serious life and economic losses to people, landslide disaster monitoring has always been a key research and development project, among which the prediction of landslide damage is to reduce the effective loss of landslide disasters. [0003] Intelligent prediction and early warning technology supported by big data is rapidly emerging with the advent of the era of artificial intelligence, but the early warning technology for landslides generally does not take into account the timing of landslide monitoring data, so the effect is not ideal. [0004] At present, the landslide early warning technology based on machine learning is general...

AI Technical Summary

Problems solved by technology

[0003] Intelligent prediction and early warning technology supported by big data is rapidly emerging with the advent of the era of artificial intelligence, but the landslide early warning technology generally does not take into account the timing of landslide monitoring data, so the effect is not ideal

[0004] At present, landslide early warning technology based on machine learning is generally not deep enough, including method selection, parameter adjustment, optimization, and early warning product implementation are very difficult

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