Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash–Sutcliffe efficiency (NSE): 0.599–0.962) outperforms the MLR model (NSE: 0.584–0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.

Oil and water separation due to gravitation is extensively applied in water and wastewater treatment to get rid of the oil droplets. Optimum inlet baffle configuration may facilitate the formation of favorable flow fields and heighten the separator tank’s removal efficiency. Experimental tests were carried out to ascertain appropriate position for an inlet baffle in a rectangular separator tank. There are four parts in the experimental measurements. First of all, measured velocity fields make use of an acoustic Doppler velocimeter. Second of all, the uniformity of flow is measured by drawing a comparison of the standard deviation of the velocity profile. The oil concentration at both inlet and outlet points was later on assessed. Finally, the separation tank’s removal efficiency was evaluated. The results provide an indication that a uniform flow pattern improved when the inlet baffle position gave a minimum standard deviation value. In this case, the minimum concentration of oil at the outlet and the highest removal efficiency have been obtained.