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

Xuenan Li, Kun Han, Wenhe Liu, Tieliang Wang, Chunsheng Li (), Bin Yan, Congming Hao, Xiaochen Xian and Yingying Yang
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Xuenan Li: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Kun Han: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Wenhe Liu: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Tieliang Wang: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Chunsheng Li: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Bin Yan: College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
Congming Hao: Center of Engineering Construction Service, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
Xiaochen Xian: Liaoning Agricultural Development Service Center, Shenyang 110030, China
Yingying Yang: Liaoning Agricultural Development Service Center, Shenyang 110030, China

Sustainability, 2025, vol. 17, issue 6, 1-17

Abstract: 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.

Keywords: farmland water conservancy projects; principal component analysis; support vector regression; dung beetle optimizer; cost indicators (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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