TITLE:
Integrating Remote Sensing and Machine Learning to Evaluate Riverbank Instability in the Akanyaru Transboundary Region
AUTHORS:
Dyna Uwambajimana, Stephen Uwamahoro, Frank Nsimire, Elias Nyandwi, Angelique Nishyirimbere, Sabato Nzamwita, Isaac Nzayisenga, Stephen Mbewe, Bertil Nlend, Sèlomè Karen de Lespérance Ague, Katabarwa Murenzi Gilbert
KEYWORDS:
Riverbank Instability, Akanyaru River, Linear Regression Model, Supervised Classification, Ecosystem Services Loss, Rwanda and Burundi
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.9,
September
15,
2025
ABSTRACT: 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 satellite-based early warning systems, revising buffer policies, promoting agroforestry near riparian zones, and integrating high-resolution socioeconomic data into future modelling. These insights strengthen riverbank resilience strategies and advance Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land).