In the rugged landscapes of Central Ethiopia, a silent threat lurks beneath the surface, one that could significantly impact the region’s burgeoning energy sector. Landslides, triggered by a complex interplay of geological and environmental factors, pose a substantial risk to infrastructure and human life. However, a groundbreaking study led by Migartu Abdissa Tukku from the Department of Applied Geology at Adama Science and Technology University is shedding new light on these hazards, offering a beacon of hope for safer development.
Tukku and his team have meticulously mapped landslide susceptibility in the Abuna Gindeberet area, West Shoa Zone, using an integrated approach that combines geospatial analysis and statistical modeling within a GIS framework. Their work, recently published, provides a detailed understanding of the causative factors behind landslides, paving the way for more informed decision-making in the energy sector.
The study documents a staggering 1,222 landslides, with 70% used for model training and the remaining 30% for validation. By analyzing factors such as slope, aspect, curvature, lithology, land use-land cover, distance to lineament, distance to stream, and rainfall, the researchers have developed comprehensive landslide susceptibility maps (LSMs). These maps categorize the region into very low, low, moderate, high, and very-high susceptibility classes, offering a clear picture of where and why landslides are most likely to occur.
“Understanding the spatial distribution of landslide susceptibility is crucial for mitigating risks and planning sustainable development,” Tukku explains. “Our study provides a robust framework for identifying high-risk areas, which is particularly relevant for the energy sector, where infrastructure development is often in remote and challenging terrains.”
The researchers employed two statistical approaches—Frequency Ratio (FR) and Logistic Regression (LR)—to weigh the influence of each causative factor. The FR model revealed that very high susceptibility areas cover 12% of the region, while the LR model indicated a slightly higher 23%. Both models highlighted that landslides are primarily associated with moderately sloped terrains, sandstone-rich areas, and regions with agricultural activities and sparse forests near lineaments.
The implications for the energy sector are profound. As Ethiopia continues to invest in hydroelectric power and other energy infrastructure, the ability to predict and mitigate landslide risks becomes increasingly important. By identifying high-risk areas, energy companies can make more informed decisions about where to build, how to design their infrastructure, and how to implement effective monitoring and maintenance strategies.
The study’s validation through the ROC-AUC method showed impressive success rates of 0.857 for the FR model and 0.849 for the LR model, underscoring the reliability of the researchers’ findings. These results, published in the journal ‘Scientific African’ (translated from Amharic as ‘African Science’), represent a significant step forward in landslide hazard assessment.
As the energy sector continues to expand in Ethiopia and beyond, the need for accurate and reliable landslide susceptibility maps becomes ever more pressing. Tukku’s work offers a blueprint for future research and practical applications, highlighting the importance of interdisciplinary approaches in tackling complex environmental challenges. By bridging the gap between geospatial analysis and statistical modeling, this study not only advances our understanding of landslide hazards but also paves the way for safer, more sustainable development in the energy sector and beyond.