An approach using random forest intelligent algorithm to construct a monitoring model for dam safety


The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.

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This research has been partially supported by the National Key Research and Development Program of China (SN: 2018YFC0407101, 2016YFC0401601, 2017YFC0804607), National Natural Science Foundation of China (SN: 51739003, 51579083, 51479054), Key R&D Program of Guangxi (SN: AB17195074), Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (SN: 20165042112, 20145027612), the Fundamental Research Funds for the Central Universities (SN: 2018B40514, 2015B25414).

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Correspondence to Zhiping Wen.

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Li, X., Wen, Z. & Su, H. An approach using random forest intelligent algorithm to construct a monitoring model for dam safety. Engineering with Computers 37, 39–56 (2021).

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  • Dam safety
  • Monitoring model
  • Random forest
  • Support vector machine
  • Significance test