Wider adoption of machine learning methods in water resources has the potential to greatly accelerate the efficiency and quality of analysis. The Nile River is one of the major fluvial hydro-systems in the world. Fluvial islands are present in nearly all natural and regulated rivers. The Nile River is characterized by numerous natural phenomena and human interventions represented in multiple islands characteristics. This paper investigates the formation and development of the Nile River islands in the fourth reach, which extends between Assuit and Delta barrages. A machine learning (ML) technique, with the Random Forest (RF) algorithm, has been introduced as a potential technique to replace the traditional ones, to extract and classify the land cover types and the geometrical characteristics of the Nile River islands. The assessment of the results of extracting the Nile River islands and the land cover types are included. The accuracy of the extracted boundaries of the islands is assessed using field surveying data. The classification of the islands based on the islands' geometric characteristics represented that 70% of the extracted islands are classified as Wide Island, 20% are classified as Equal Island, and 10% as Narrow Island. The islands’ classification, based on the land cover, results show that there is only 5% of the islands that are urban areas, 5% of the islands are mixed class (both vegetation and urban), and the rest of the islands 90% have a vegetation land cover type. The accuracy assessment was performed using the error matrix, the results show that the overall accuracy of the land cover classification is greater than 84%. The proposed islands’ classification scheme can become an important tool that provides the decision-makers with more detailed information to improve the planning of the Nile River islands development projects. Furthermore, this schema can be expanded to other climatic and topographic regions.

In terms of the significance of incorporating smart techniques into large river navigation systems to increase inland navigation competitiveness, this article introduces the development of smart River Information Services (RIS) in Egypt based on smart information and communication technologies as real-time tracking systems, Open Geographic Information Systems, and Nile River Information Systems, as well as proposes measures to tackle the challenges that may be faced, compared with experiences and knowledge of RIS in large river smart navigation systems. This comparison creates new frameworks for the future development of using smart technology in inland waterways in Egypt. It could be concluded that advanced navigation technologies and infrastructures are still being developed in Egypt and are not yet available. Almost all of Egypt's Vessel Traffic Services station equipment and applications are imported from Europe. Undoubtedly, there will be at least a 5- to 10-year time lag for Egypt to develop independent RIS to its full extent. It is necessary to keep good cooperation with the European and Chinese inland sector and refer to their experience and knowledge.

Egypt is one of the countries that will face significant challenges in the coming years, especially with dam projects, climate changes, and sea-level rise. These challenges may lead to water shortage or lead to excess inflow water according to the operation rules for these dams. As a result, many considerations must be made in order to face these challenges. One of them, which is the focus of this research, is studying the impact of the extra discharge that can be released downstream of the High Aswan Dam to manage disaster considering the dam operation restrictions. Two-dimensional mathematical model (Delft3D) is used to predict the water surface profile associated with high discharge, which is about 350 m.m3/day under different scenarios of Barrage operation rules in the study area. The Great Cairo Region, where significant projects such as tourism, water, and power plants have been chosen to carry out this study, is the most critical and active area. For each scenario, the predicted water level and its impact on human properties and habitations is analyzed. In addition, many other side effects on the river behavior, such as aggradations, degradation, bank erosion and inundation are evaluated. An application was developed using the Python programming language and GIS to store predicted water levels and assess the database for the river's vulnerable facilities. Finally, the study will propose a strategy for managing and mitigating flood hazards.