Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.

Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly explored version of an ML model called the long short-term memory (LSTM) was investigated for streamflow prediction using historical data for forecasting for a particular period. For a case study located in a tropical environment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. The modelling was performed according to several perspectives: (i) The feasibility of applying the developed LSTM model to streamflow prediction was verified, and the performance of the developed LSTM model was compared with the classic backpropagation neural network model; (ii) In the experimental process of applying the LSTM model to the prediction of streamflow, the influence of the training set size on the performance of the developed LSTM model was tested; (iii) The effect of the time interval between the training set and the testing set on the performance of the developed LSTM model was tested; (iv) The effect of the time span of the prediction data on the performance of the developed LSTM model was tested. The experimental data show that not only does the developed LSTM model have obvious advantages in processing steady streamflow data in the dry season but it also shows good ability to capture data features in the rapidly fluctuant streamflow data in the rainy season.