Asian Journal of Geographical Research

This study evaluates shoreline erosion and accretion dynamics along the Orashi River in Ahoada West LGA, Rivers State, Nigeria, using advanced geospatial tools and machine learning techniques. Focusing on the Orashi River, a critical yet understudied region, the research analyzes Landsat imagery from 1974 to 2024, sourced from the United States Geological Survey (USGS), to quantify changes in shoreline area. Machine learning was employed for trend analysis and predictive modeling, while ArcGIS 10.6 facilitated spatial analysis and visualization. Results reveal significant erosion over 30 years, with the shoreline area declining from 9.09 km² in 1974 to 5.05 km² in 2004. A temporary accretion phase occurred in 2014 (7.11 km²), followed by renewed erosion in 2024 (6.07 km²). Projections indicate further erosion, with an expected shoreline area of 5.23 km² by 2034. Erosion rates, defined as the percentage loss of shoreline area, peaked at 82% between 1974 and 1984, while the deposition rate, representing accretion, reached 99% from 2004 to 2014 The study highlights the geological processes driving these changes and underscores the value of geospatial tools in quantifying and visualizing shoreline trends. The integration of remote sensing, GIS, and machine learning provides a robust framework for predictive modeling, enabling proactive strategies for sustainable coastal management. The findings have significant implications for policy and management, emphasizing the need for continuous monitoring, climate change adaptation, and coastal protection measures to mitigate the impacts of shoreline dynamics on vulnerable ecosystems and communities.