Prediction Model of Farmland Water Conservancy Project Cost Index Based on PCA–DBO–SVR

With the gradual cessation of budget quota standards and the emphasis on market-based pricing, accurately predicting project investments has become a critical issue in construction management. This study focuses on cost indicator prediction for irrigation and drainage projects to address the absence of cost standards for farmland water conservancy projects and achieve accurate and efficient investment prediction. Engineering characteristics affecting cost indicators were comprehensively analyzed, and principal component analysis (PCA) was employed to identify key influencing factors. A prediction model was proposed based on support vector regression (SVR) optimized using the dung beetle optimizer (DBO) algorithm. The DBO algorithm optimized SVR hyperparameters, resolving issues of poor generalization and long prediction times. Validation using 2024 farmland water conservancy project data from Liaoning Province showed that the PCA–DBO–SVR model achieved superior performance. For electromechanical well projects, the root mean square error (RMSE) was 1.116 million CNY, mean absolute error (MAE) was 0.910 million CNY, mean absolute percentage error (MAPE) was 3.261%, and R 2 reached 0.962. For drainage ditch projects, RMSE was 0.500 million CNY, MAE was 0.281 million CNY, MAPE was 3.732%, and R 2 reached 0.923. The PCA–DBO–SVR model outperformed BP, SVR, and PCA–SVR models in all evaluations, demonstrating higher prediction accuracy and better generalization capability. This study provides theoretical support for developing cost indicators for farmland water conservancy projects and offers valuable insights for dynamically adjusting national investment standards and improving construction fund management.