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<p>The South-to-North Water Diversion Project is a critical strategic infrastructure initiative in China designed to alleviate water scarcity in the North China Plain. However, the excavation and nesting behaviors of termites pose severe risks to embankment safety. To investigate the relationship between termite nest scale and structural parameters—and to develop an effective predictive model—this study analyzed 2024 termite nest survey data from the project’s Central Route, employing correlation and regression analyses. Key variables were screened via correlation analysis, with multicollinearity assessed to guide model simplification. A generalized additive model (GAM) was adopted to predict primary nest size, while multiple linear regression (MLR) modeled secondary nest count. Low-cost, feasible predictors (e.g., tunnel width and nest depth) were prioritized to enhance practicality. Primary nest size exhibited nonlinear positive correlations with nest depth, tunnel width, queen length, and secondary nest count. Secondary nest count showed linear positive correlations with nest depth, tunnel width, and queen length. For field applications, non-invasive measurements (tunnel width and nest depth) were identified as optimal predictors, balancing accuracy and operational feasibility. Both GAM (primary nest) and MLR (secondary nests) demonstrated high goodness-of-fit. Termite nest scale can be reliably predicted using tunnel width and nest depth as key variables. This approach offers a cost-effective, field-ready solution for embankment risk assessment in large-scale water diversion projects.</p>

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Predictive modeling of termite nest size in the middle route of the South-to-North Water Diversion Canal based on multi-feature variables

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Zhang Xia,
  • Li Linjie,
  • You Jiahe,
  • Ouyang Kai,
  • Liu Qinwen

摘要

The South-to-North Water Diversion Project is a critical strategic infrastructure initiative in China designed to alleviate water scarcity in the North China Plain. However, the excavation and nesting behaviors of termites pose severe risks to embankment safety. To investigate the relationship between termite nest scale and structural parameters—and to develop an effective predictive model—this study analyzed 2024 termite nest survey data from the project’s Central Route, employing correlation and regression analyses. Key variables were screened via correlation analysis, with multicollinearity assessed to guide model simplification. A generalized additive model (GAM) was adopted to predict primary nest size, while multiple linear regression (MLR) modeled secondary nest count. Low-cost, feasible predictors (e.g., tunnel width and nest depth) were prioritized to enhance practicality. Primary nest size exhibited nonlinear positive correlations with nest depth, tunnel width, queen length, and secondary nest count. Secondary nest count showed linear positive correlations with nest depth, tunnel width, and queen length. For field applications, non-invasive measurements (tunnel width and nest depth) were identified as optimal predictors, balancing accuracy and operational feasibility. Both GAM (primary nest) and MLR (secondary nests) demonstrated high goodness-of-fit. Termite nest scale can be reliably predicted using tunnel width and nest depth as key variables. This approach offers a cost-effective, field-ready solution for embankment risk assessment in large-scale water diversion projects.