Osama Ragab Ibrahim

Climate change is increasingly considered to be a global threat to the environment and human activities. Therefore, raising awareness of climate change and incorporating it into the educational curriculum is crucial. This study investigated social studies and science teachers’ perceptions of various aspects of climate change education in Oman. Data were collected from 311 social studies and science teachers (Grades 5-12) using an online survey. The results showed that teachers agreed on many statements regarding climate change, including that climate change is happening; that human activities are the main cause of climate change, that there are risks of climate change for Oman, and that learning about climate change is important for students. The instructional methods most commonly used by teachers in both groups were discussion, current events, and small groups. On the other hand, the least used instructional methods were inviting climate experts/speakers to the classroom, field trips, and debates. Less emphasis was placed on certain teaching resources, such as geographical information systems and remote sensing. The study identified several challenges and opportunities related to teaching climate change. No statistically significant differences were found between social studies and science teachers’ perceptions of climate change education except in terms of teaching resources and opportunities for teaching climate change, which were significantly higher among social studies teachers. The findings of this study offer valuable information for curriculum developers on climate change education.

Accurate reservoir inflow predictions are critical for effective flood control and optimizing hydropower generation, thereby enhancing water resource management. This study introduces an advanced hydrological modeling approach that leverages a basic recurrent neural network (bRNN), convolutional neural network (CNN) with gated recurrent units (GRU) (bRNN-CNN-GRU), GRU with long short-term memory (LSTM) (GRU-LSTM) hybrid models, and deep neural network (DNN) to predict daily reservoir inflow at the Sefid Roud Dam. By utilizing historical data from 2018 to 2024, this study examined the following two multivariate scenarios: one incorporating water parameters such as water level, evaporation, and temperature extremes, and another focused solely on inflow delays. Training and testing sets were created from the dataset, with 80% for training and 20% for testing. For benchmarking purposes, the performance of the bRNN-CNN-GRU was evaluated against a deep neural network (DNN) and a GRU-LSTM hybrid. The evaluation metrics used were root mean square error (RMSE), correlation coefficient (r), and Nash Sutcliffe coefficient (NSE). Results demonstrated that, while all models performed better under the scenario incorporating inflow delays, the bRNN-CNN-GRU model achieved the best performance, with an RMSE of 0.71, r of 0.97, and NSE of 0.95, outperforming both the DNN and GRU-LSTM models. These findings highlight the significant advancements in hydrological modeling and affirm the applicability of the bRNN-CNN-GRU model for improved reservoir management in diverse settings.