Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project

Abstract Accurate construction cash flow forecasting is very important in successfully managing cost during execution of building projects. Despite many research efforts, it still remains a difficult issue in attaining an accurate forecast model of cash flows due to the risk factors and characteristic of the project. Additionally, cash flow of the construction projects is strongly impacted by sequence and non-sequence factors. Hence, this study proposed a novel artificial intelligence(AI)-based inference model, named symbiotic organisms search-optimized neural network-long short-term memory (SOS-NN-LSTM), which employs symbiotic organisms search (SOS) algorithm to obtain the suitable hyperparameters of the neural network (NN) and long short-term memory (LSTM) for establishing a robust hybridization model. In the proposed model, the LSTM technique addresses time series problem with considering the complexity of projects while the NN technique aims at tackling non-sequence factors. The experimental results on 13 construction projects have supported the SOS-NN-LSTM as the best model in forecasting the cash flow by achieving the greatest values of (2.55%), MAPE (5.71%), MAE (2.07%), and R2 (0.983). The statistical result further reveals that accuracy of cash flow forecasting can be improved at least 13.4% and 12.0% in terms of RMSE and MAE, respectively, in comparison with other comparative AI-based inference models. The SOS-NN-LSTM model is thus a useful tool to help managers forecast and control cash flow of construction projects.