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.

在水资源中更广泛地采用机器学习方法有可能大大提高分析的效率和质量。尼罗河是世界上主要的河流水利系统之一。河流岛屿几乎存在于所有自然和受管制的河流中。尼罗河的特点是众多的自然现象和以多个岛屿特征为代表的人为干预。本文研究了第四河段尼罗河岛屿的形成和发展，该河段延伸在 Assuit 和三角洲拦河坝之间。机器学习 (ML) 技术和随机森林 (RF) 算法已被引入作为替代传统技术的潜在技术，用于提取和分类尼罗河岛屿的土地覆盖类型和几何特征。包括对提取尼罗河岛屿和土地覆被类型结果的评估。使用实地测量数据评估提取的岛屿边界的准确性。根据岛屿的几何特征对岛屿进行分类，提取的岛屿中，70%为宽岛，20%为等岛，10%为窄岛。岛屿的分类，基于土地覆盖，结果显示只有 5% 的岛屿是城市地区，5% 的岛屿是混合类（植被和城市），其余岛屿 90%有植被土地覆盖类型。使用误差矩阵进行精度评估，结果表明土地覆被分类的总体精度大于84%。提议的岛屿分类方案可以成为一个重要工具，为决策者提供更详细的信息，以改进尼罗河岛屿开发项目的规划。此外，该模式可以扩展到其他气候和地形区域。