Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area

Author:

Zeng Taorui,Yin Kunlong,Jiang Hongwei,Liu Xiepan,Guo Zizheng,Peduto Dario

Abstract

AbstractThe monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference56 articles.

1. Li, Y., Wu, L., Chen, X. & Zhou, W. Impacts of three gorges dam on regional circulation: A numerical simulation. J. Geophys. Res. Atmos. 124, 7813–7824 (2019).

2. Liang, X. et al. Characterizing the development pattern of a colluvial landslide based on long-term monitoring in the three gorges reservoir. Remote Sens.-Basel. 13, 224 (2021).

3. Luo, H., Tang, H., Zhang, G. & Xi, W. The influence of water level fluctuation on the bank landslide stability. Earth Sci. J. China Univ. Geosci. 33, 687–692 (2008).

4. Krkač, M., Bernat Gazibara, S., Arbanas, Z., Sečanj, M. & Mihalić Arbanas, S. A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides. 17, 2515–2531 (2020).

5. Huang, F., Yin, K., Zhang, G., Zhou, C. & Zhang, J. Landslide groundwater level time series prediction based on phase space reconstruction and wavelet analysis-support vector machine optimized by Pso algorithm. Earth Sci. J. China Univ. Geosci. 40, 1254–1265 (2015).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3