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

Property development during the COVID-19 pandemic: challenges and outlook in Malaysia

(2022) Zainab T. Jagun et al.   ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction

(2022) Odey Alshboul et al.   Sustainability

An interpretable machine learning approach for evaluating the feature importance affecting lost workdays at construction sites

(2022) Kyung-Su Kang et al.   Journal of Building Engineering

A novel elastic net-based NGBMC(1,n) model with multi-objective optimization for nonlinear time series forecasting

(2021) Lang Yu et al.   Communications in Nonlinear Science and Numerical Simulation

SPlit: An Optimal Method for Data Splitting

(2021) V. Roshan Joseph et al.   TECHNOMETRICS

Model-Based Dynamic Forecasting for Residential Construction Market Demand: A Systemic Approach

(2021) Kyeong-Baek Kim et al.   Applied Sciences-Basel

A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

(2019) Ebrahim Eslami et al.   NEURAL COMPUTING & APPLICATIONS

How affordable housing becomes more sustainable? A stakeholder study

(2017) Xiaolong Gan et al.   JOURNAL OF CLEANER PRODUCTION

Using genetic algorithms and linear regression analysis for private housing demand forecast

(2007) S. Thomas Ng et al.   BUILDING AND ENVIRONMENT