Integrating Remote Sensing and Machine Learning to Evaluate Riverbank Instability in the Akanyaru Transboundary Region

Riverbank instability poses a mounting global threat, especially across East Africa's transboundary river systems, where geospatial assessments remain scarce.This study applies advanced remote sensing and machine learning regression techniques to quantify riverbank displacement along the Akanyaru River (Rwanda-Burundi) from 2004 to 2024, using Landsat and Sentinel-1 imagery, elevation data, and open-source geospatial platforms (Google Earth Engine, QGIS, Python).Key objectives include: 1) assess spatial degradation and extent of riverbank shifts, 2) forecast future river flow displacement for 2034, 3) examine the relationship between land use/land cover (LULC), slope, and flood inundation, and 4) evaluate buffer zone protection policies and socioeconomic effects on adjacent communities Results reveal marked spatial variation, including predicted reversal at Location B (-1203.5 m), persistent erosion at Location A (+950 m), and relative stability at Location C (+517 m).Agricultural encroachment and low-slope terrain exacerbate degradation and flooding, leading to 400.69 hectares of cropland loss and ecosystem service depletion valued at $2.23 million.Despite formal environmental laws in both Rwanda and Burundi, enforcement remains weak due to vague buffer zone boundaries and minimal community engagement.The study recommends deploying sat-