The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km². The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

A reliable and continuous streamflow simulation capability is essential for systematic management of water resource systems. Thus, predicting streamflow is important for water management and flood control. This study evaluated the effectiveness of a few data-driven procedures, such as the least squares support vector machine (LS-SVM), M5P tree, and random forest (RF) algorithm for estimating streamflows of the Bernam and Tualang rivers of Malaysia. Three standard statistical measures, i.e., correlation coefficient (CE), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performance of the developed model. The performance of RF-based models was found to be higher than that of LS-SVM and M5P-based models with respect to predicting streamflow for both the rivers.

The attributes of effective teaching in higher education remains controversial and has never been conclusive. The purpose of this study is to determine the factors affecting the students’ perceptions of teaching effectiveness, and how the instructor and course attributes can significantly influence teaching effectiveness as measured by students in course evaluation surveys. The study analyzed 3,798 student evaluations of faculty at Jubail University College using factor analysis to find out the factors loading and average extract variance value. The study predicted that there is a significant relationship between the five dimensions of teaching and students’ ratings of teaching effectiveness (i.e. instructor’s personality, knowledge, teaching ability, marking and grading policy, and course attributes and learning outcomes). The findings support the hypothesis that there is a significant relationship between effective teaching dimensions and students ratings. The study contributes to the body of literature on evaluation of teaching effectiveness in Saudi higher education.