The contribution of the frequency ratio model and the prediction rate for the analysis of landslide risk in the Tizi N'tichka area on the national road (RN9) linking Marrakech and Ouarzazate

Abstract

Road infrastructure is vital for economic development, connecting various locations. However, in Morocco, landslides pose recurring challenges to road projects due to factors like lithology, climate, rift structures, and high altitudes in the High Atlas Mountains. Developing susceptibility maps for landslides is crucial to anticipate and take appropriate actions. National Road Number 9, particularly the Tizi N'tichka region in the High Atlas, experiences significant landslide issues. This study used frequency ratio (RF) and prediction rate (PR) techniques to map landslide susceptibility along this road section connecting Marrakech and Ouarzazate. By analyzing thirteen factors, including elevation, curvature, precipitation, slope, and land use, a landslide sensitivity index (LSI) was created. A landslide inventory of 214 locations was prepared, with 70% (150 events) used for model training and 30% (64 events) for validation. The resulting sensitivity map classified areas into five categories of landslide susceptibility. The model's effectiveness was evaluated using the receiver operating characteristic (ROC) curve technique, yielding an AUC value of 92.3% during validation. The findings demonstrate the efficacy of RF and PR methods in landslide susceptibility mapping. It helps identify high-risk areas along National Road Number 9 in the Tizi N'tichka region, enabling proactive measures for mitigating landslide impacts on road infrastructure. These outcomes serve as a foundational basis for conducting further research and verifying the performance of alternative methods such as the susceptibility index model (SII), the inventory validation (IV) model, and the weight of evidence (WoE) model Overall, this research contributes to better anticipation and management of landslides, ensuring the reliability and safety of road networks in the studied region and beyond the geographical boundaries.