Forecasting demand in the residential construction industry using machine learning algorithms in Jordan

IF 3.1 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Farouq Sammour, Heba Alkailani, Ghaleb J. Sweis, R. Sweis, Wasan Maaitah, Abdulla Alashkar
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引用次数: 1

Abstract

Purpose Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan. Design/methodology/approach The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination. Findings Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022. Originality/value The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.
使用机器学习算法预测约旦住宅建筑业的需求
需求预测是计划工作的关键组成部分,对管理核心业务至关重要。本研究旨在评估几种机器学习(ML)算法的使用,以预测约旦住宅建设的需求。设计/方法/途径与住宅建筑需求相关的变量和机器学习算法的识别和选择通过文献综述进行了说明。特征选择采用逐步反向消去法。通过将机器学习预测结果与实际残差值进行比较,并基于确定系数进行比较,证明了该算法的准确性。选取9个经济指标建立需求模型。Elastic-Net的准确率最高(0.838),人工神经网络的准确率为(0.727),其次是Eureqa的准确率(0.715)和Extra Trees的准确率(0.703)。根据最佳表现模型预测的结果,约旦2023年第一季度住宅建筑需求预计将比2022年同期增长11.5%。原创性/价值本研究的结果通过识别约旦住宅建筑业中最具影响力的变量扩展到现有的知识体系。此外,所开发的模型将使建筑工程领域的用户能够做出可靠的需求预测,同时也有助于有效的财务决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Construction Innovation-England
Construction Innovation-England CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
7.10
自引率
12.10%
发文量
71
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