Explainable machine learning for predicting cost and schedule overruns in irrigation projects: A safety-driven approach

Abstract

Irrigation projects are essential for ensuring water security and sustainable agricultural development; however, they are highly susceptible to human, managerial, equipment, and environmental risks that contribute to cost overruns and schedule delays. Although machine learning (ML) has been widely applied to project performance prediction, its integration with validated safety indicators and explainable artificial intelligence for irrigation projects remains largely unexplored. Therefore, this study proposes an explainable machine learning framework for predicting cost overruns and schedule delays using safety indicators. A comprehensive dataset comprising 50 irrigation projects implemented in Iraq between 2022 and 2025 was developed. Twenty-three safety indicators were identified through a systematic literature review, refined through expert consultation, and validated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Four ML algorithms, namely Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), were developed and evaluated using 5-fold cross-validation. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The cross-validation results demonstrated that Random Forest achieved the most robust predictive performance for both cost overruns (R² = 0.912, RMSE = 1.685) and schedule delays (R² = 0.894, RMSE = 3.236). Furthermore, SHAP analysis identified human-related factors as the primary drivers of cost overruns and managerial factors as the dominant contributors to schedule delays. The proposed framework provides an interpretable decision-support tool for proactive safety management, early risk identification, and improved planning of irrigation projects.