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Updated: Jan 11, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Risk-indexed artificial neural network for predicting duration and cost of irrigation canal-lining projects using
Boshra Taha1,2, Ahmed H Ibrahim3, Asmaa A Soliman4
1Industrial Engineering Department, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, Saudi Arabia. Boshrataha@zu.edu.eg.
None:
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 forecasts during early project stages. This research introduces an integrated ANN-based framework that combines expert-driven risk assessment with machine learning, providing a practical decision-support tool for infrastructure projects.
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01:27Design Example: Design of an Irrigation Channel
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.