This study is focused on the use of random forest (RF) to forecast the streamflow in the Kesinga River basin. A total of 169 data points were gathered monthly for the years 1991–2004 to create a model for streamflow prediction. The dataset was allotted into training and testing stages using various ratios, such as 50/50, 60/40, 70/30, and 80/20. The produced models were evaluated using three statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC). The analysis of the models' performances revealed that the training and testing ratios had a substantial impact on the RF model's predictive abilities; models performed best when the ratio was 60/40. The findings demonstrated the right dataset ratios for precise streamflow prediction, which will be beneficial for hydraulic engineers during the water-related design and engineering stages of water projects.

Plunging jets are popular for aeration in wastewater treatment plants due to low energy needs. In the present study, square jets were evaluated to determine their effectiveness in providing aeration efficiency by plunging jets in an open channel flow. The study seeks to add to the existing knowledge and provide insight into the performance of single and multiple square plunging jets in wastewater treatment plants. Passing a water sample from the pool through an open channel, via square water jet(s), has shown that variables such as a higher number of jets, and discharge have a significant impact on aeration efficiency (E₂₀). The findings demonstrate that the increase in E₂₀ was found to be 85.22% to 117.6% with Nj=64 over the single jet when discharge was varied from 3.41L/s to 4.75L/s, further affirming the remarkable effectiveness of plunging jets in promoting aeration in open channel flows. Furthermore, it was observed that Fr. No. is the parameter that has the most impact on calculating E₂₀ with correlation of 0.95311.