An OWOA-RFWSVR-DLM-based Model for Predicting Dam Deformation in the Alpine Region

GE Pan-meng; CHEN Bo; CHEN Wei-nan; ZHU Ming-yuan

doi:10.11988/ckyyb.20211348

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Journal of Changjiang River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (5) : 153-159. DOI: 10.11988/ckyyb.20211348

Engineering Safety and Disaster Prevention

An OWOA-RFWSVR-DLM-based Model for Predicting Dam Deformation in the Alpine Region

GE Pan-meng^1,2, CHEN Bo^1,2, CHEN Wei-nan^1,2, ZHU Ming-yuan³

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Abstract

Due to the protection of surface thermal insulation layer, the internal temperature of concrete dam in cold region lags behind the air temperature. When internal thermometers are lacking, HST (hydrostatic-seasonal-time) model generates large error in predictions; even when internal thermometers are present, MR (multiple regression) model can not refelct the nonlinear relation between temperature and deformation. To address the problem of inaccurate deformation prediction in alpine regions, we propose to use a support vector regression (SVR) weighted by the RReliefF algorithm and DLM(Distribution Lag Model) temperature factors to predict dam displacement. The necessary hyperparameters are optimized using an improved whale swarm approach with OBL (Opposition-based Learning). By comparing the performance of the proposed model with MR and other machine learning algorithms, we found that the proposed model has higher prediction accuracy and better reflects the working characteristics of an insulated concrete dam.

Key words

Regression ReliefF Factor Weighted Support Vector Machine Regression / Opposition-based Learning Whale Optimization Algorithm / DLM / dam deformation prediction / alpine regions

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GE Pan-meng, CHEN Bo, CHEN Wei-nan, ZHU Ming-yuan. An OWOA-RFWSVR-DLM-based Model for Predicting Dam Deformation in the Alpine Region[J]. Journal of Changjiang River Scientific Research Institute. 2023, 40(5): 153-159 https://doi.org/10.11988/ckyyb.20211348

References

[1] 邓铭江. 严寒地区碾压混凝土筑〗坝技术及工程实践[J]. 水力发电学报, 2016, 35(9): 111-120.
[2] 刘毅, 杜雷功, 钱文勋, 等. 高寒区高混凝土坝关键技术难题与解决途径[J]. 水利水电技术, 2020, 51(3): 45-52.
[3] WILLM G,BEAUJOINT N.Les Méthodes de Surveillance des Barrages au Service de la Production Hydraulique d'Electricité de France,Problèmes Anciens et Solutions Nouvelles[C]//IXth International Congress on Large Dams. ICOLD. Turkey,Istanbul.September 4-8, 1967:529-550.
[4] 钱秋培, 崔伟杰, 包腾飞, 等. 基于SVM的混凝土坝变形监控模型预测能力实例分析[J]. 长江科学院院报, 2018, 35(8): 46-50.
[5] 李桥, 巨能攀, 黄健, 等. 基于EEMD与SE的IPSO-LSSVM模型在坝肩边坡变形预测中的应用[J]. 长江科学院院报, 2019, 36(12): 47-53.
[6] 曾永军, 张俊文, 曹登刚, 等. RS-RF模型在混凝土坝变形预测中的应用[J]. 水利水电技术(中英文), 2021, 52(5): 82-88.
[7] 王文娜, 吴侃, 陈冉丽, 等. 基于GIS和随机森林的采动区建筑物损害综合评价[J]. 煤炭科学技术, 2022, 50(3): 201-207.
[8] 胡江. 特高拱坝运行初期变形监测预报模型及构建方法[J]. 水利水运工程学报, 2020(5): 63-71.
[9] SAMADI S H,GHOBADIAN B,NOSRATI M. Prediction of Higher Heating Value of Biomass Materials Based on Proximate Analysis Using Gradient Boosted Regression Trees Method[J]. Energy Sources,Part A:Recovery,Utilization, and Environmental Effects,2021,43(6):672-681.
[10] 李龙起, 王梦云, 赵皓璆, 等. 基于CEEMDAN-BA-SVR-Adaboost模型的白水河滑坡位移预测[J]. 长江科学院院报, 2021, 38(6): 52-59, 66.
[11] 刘超, 唐锡彬, 邓冬梅, 等. 基于支持向量机回归的岩体变形模量预测[J]. 地质科技情报, 2018, 37(5): 275-280.
[12] 李涧鸣, 包腾飞, 高瑾瑾, 等. 基于小波EGM-ISFLA-SVR的大坝变形组合预测模型[J]. 水利水电技术, 2018, 49(5): 57-62.
[13] 易正元, 苏怀智, 杨立夫. 混凝土坝变形监控模型的随机森林与旗鱼优化组合建模方法[J]. 水电能源科学, 2021, 39(10): 106-109, 143.
[14] 刘凯歌,韦笃取.基于WOA-ESN的电机系统混沌振荡预测[J/OL].计算物理:1-9(2021-11-25)[2021-12-16].http://kns.cnki.net/kcms/detail/11.2011.O4.20211028.1510.002.html.
[15] 汪峰,毛锦伟,刘章军.联合鲸鱼算法和遗传算法优化GRNN预测斜拉索覆冰厚度[J/OL].土木与环境工程学报(中英文):1-10(2021-11-25)[2021-12-16]. http://kns.cnki.net/kcms/detail/50.1218.TU.20210812.1603.006.html.
[16] TIZHOOSH H R.Opposition-based Learning:A New Scheme for Machine Intelligence[C]//Proceedings of the IEEE International Conference of Intelligent Agents, Web Technologies and Internet Commerce. Vienna:Vienna IEEE Press.September 19-22, 2005:695-701.
[17] CHOU J S, PHAM A D. Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering[J]. Computer-Aided Civil and Infrastructure Engineering, 2015, 30(9): 715-732.
[18] ALMON S.The Distributed Lag Between Capital Appropriations and Expenditures[J].Econometrica,1965,33:179-196.
[19] BRAGA A,ZANOBETTI A,SCHWARTZ J. The Time Course of Weather-related Deaths[J]. Epidemiology,2001,12(6):662-667.