The North-Western RIF of Morocco is considered as one of the most mountainous zone in the Middle East and North Africa. This area is more serious in the corridor faults region, where the recent reactivation of those tectonic layering may greatly contribute to the triggering of landslides. The consequences of this phenomenon can be enormous property damage and human casualties. Furthermore, this disaster can disrupt progress and destroy developmental efforts of government, and often pushing nations back by many years. In our previous works of Tetouan-Ras-Mazari region, we identified the areas that are prone to landslides by different methods like Weights of Evidence (WofE) and Logistic Regression (LR). In fact, these zones are built and susceptible. Undoubtedly, the challenge to save human lives is vital. For this reason, we develop a robust monitoring model as part of an alert system to evacuate populations in case of imminent danger risks. This model is ground-based remote monitoring system consist of more than just field sensors; they employ data acquisition units to record sensor measurements, automated data processing, and display of current conditions usually via the Internet of Things (IoT). To sum up, this paper outlines a new approach of monitoring to detect when hillslopes are primed for sliding and can provide early indications of rapid and catastrophic movement. It reports also continuous information from up-to-the-minute or real-time monitoring, provides prompt notification of landslide activities, advances our understanding of landslide behaviors, and enables more effective engineering and planning efforts.  The North-Western RIF of Morocco is considered as one of the most mountainous zone in the Middle East and North Africa. This area is more serious in the corridor faults region, where the recent reactivation of those tectonic layering may greatly contribute to the triggering of landslides. The consequences of this phenomenon can be enormous property damage and human casualties. Furthermore, this disaster can disrupt progress and destroy developmental efforts of government, and often pushing nations back by many years. In our previous works of Tetouan-Ras-Mazari region, we identified the areas that are prone to landslides by different methods like Weights of Evidence (WofE) and Logistic Regression (LR). In fact, these zones are built and susceptible. Undoubtedly, the challenge to save human lives is vital. For this reason, we develop a robust monitoring model as part of an alert system to evacuate populations in case of imminent danger risks. This model is ground-based remote monitoring system consist of more than just field sensors; they employ data acquisition units to record sensor measurements, automated data processing, and display of current conditions usually via the Internet of Things (IoT). To sum up, this paper outlines a new approach of monitoring to detect when hillslopes are primed for sliding and can provide early indications of rapid and catastrophic movement. It reports also continuous information from up-to-the-minute or real-time monitoring, provides prompt notification of landslide activities, advances our understanding of landslide behaviors, and enables more effective engineering and planning efforts.
El Moulat et al. REF et al established a new approach for monitoring to detect sliding hillslopes.
Monitoring System Using Internet of Things For Potential Landslides
FNC/MobiSPC
Computer Science