Risk-indexed artificial neural network for predicting duration and cost of irrigation canal-lining projects using survey-based calibration and python validation.

ABSTRACT: The purpose of this study is to develop and validate a risk-driven predictive model for estimating project duration and cost in irrigation canal lining projects, where uncertainties often lead to delays and budget overruns. Ninety-three factors were first reduced to twenty using AHP-RII (Cronbach's [Formula: see text]). A multi-layer perceptron (128-64-32, ReLU, Adam, early stopping) was trained on 5000 simulated scenarios and validated on eight projects with leave-one-project-out cross-validation. The model had [Formula: see text] (training), 0.82 (testing), and made errors within the limits of 0.87 months (time) and EGP 102,500 (cost) on average.The developed model was deployed as a Python-based desktop application, enabling engineers and planners to generate accurate time and cost forec

SUBMITTER: Taha B 

PROVIDER: S-EPMC12623735 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature