<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production and life, which not only causes serious damage to infrastructure, but also creates a certain degree of fear for people. Geological disaster is an open nonlinear complex system, which has extraordinary complex geological process, formation conditions, and causes. Therefore, it makes difficulty in capturing the dynamic information and searching for the global optimal solution. Meanwhile, traditional geological disaster warning system has the deficiencies of single disaster warning and low accuracy. In order to improve the level of early warning and detection of geological disasters, this paper combined the genetic algorithm with superior performance and Support Vector Regression (SVR) algorithm to establish a feasible and credible early warning and monitoring model for geological disasters. The experimental results show that the early warning and monitoring model proposed in this paper can greatly improve the ability of geological disaster prevention and early warning, and greatly improve the level of disaster prevention and early warning, with good engineering application value.</p>
</abstract>

In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production and life, which not only causes serious damage to infrastructure, but also creates a certain degree of fear for people. Geological disaster is an open nonlinear complex system, which has extraordinary complex geological process, formation conditions, and causes. Therefore, it makes difficulty in capturing the dynamic information and searching for the global optimal solution. Meanwhile, traditional geological disaster warning system has the deficiencies of single disaster warning and low accuracy. In order to improve the level of early warning and detection of geological disasters, this paper combined the genetic algorithm with superior performance and Support Vector Regression (SVR) algorithm to establish a feasible and credible early warning and monitoring model for geological disasters. The experimental results show that the early warning and monitoring model proposed in this paper can greatly improve the ability of geological disaster prevention and early warning, and greatly improve the level of disaster prevention and early warning, with good engineering application value.

Early warning and detection of geological disasters based on intelligent genetic algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production...

Early warning and detection of geological disasters based on intelligent genetic algorithm

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 27. Juni 2022

Akzeptiert: 21. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00195

Schlüsselwörter
Intelligent genetic algorithm, Geological disaster, Early warning and detection, SVR, Engineering application

© 2023 Dan Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Early warning and detection of geological disasters based on intelligent genetic algorithm

Online veröffentlicht: 02. Juni 2023

Seitenbereich: -

Eingereicht: 27. Juni 2022

Akzeptiert: 21. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00195

SchlüsselwörterIntelligent genetic algorithm, Geological disaster, Early warning and detection, SVR, Engineering application

© 2023 Dan Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Intelligent genetic algorithm, Geological disaster, Early warning and detection, SVR, Engineering application