Abstract:
Cost forecasting and allocation is the most widely used method for managing, planning, and
addressing the objectives of goals. Ineffective cost allocation and forecasting are posing a big
challenge in road construction projects. Underestimation is one of the major challenges in the
construction phase, which leads to projects are not accomplished in desired scope, quality, time,
and budget. Therefore, the proposed study aimed to develop a road construction cost allocation
and forecasting model using supervised machine learning algorithms such as support vector
regression, random forest regression, boosting regression, k-nearest neighbor regression, and
decision tree regression to improve the performance of existing cost allocation and forecasting of
road construction projects in Ethiopia with special reference to Wolayita city municipality. Well
organized and optimal size of the dataset collected from the Wolayita city municipal
organization, which includes a five-year road construction cost recorded data from the years
2008–2013 E.C. a total of 10,158 instances with 17 features preprocessed and 9000 instances
with 14 essential features with target variable are used to develop the proposed model. Best-fit
machine learning algorithms are necessary to develop proposed road construction projects' cost
allocation and forecasting models. The researchers used a Python tool to analyze and evaluate
the proposed model, which was built on a percentage split of 80/20 training and testing of the
selected instances of the dataset. Regression performance metrics are used to compare and
identify the best model to develop for the cost allocation and forecasting models of road
construction projects. The main findings of this study are outcomes random forest regression
algorithm with an accuracy of 98.8% in the training set and 98.5% in the testing set and the
developing a smart, simplified, and efficient cost allocation and forecasting model for road
construction projects in Ethiopia with the system prototype and tested the cost allocation and
forecasting ability
