Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study.

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.