GIS-based fuzzy logic technique for mapping landslide susceptibility analyzing in a coastal soft rock zone.

Coastal landslides pose significant threats to critical infrastructure in energy-dependent regions like the Assaluyeh anticline. To safeguard communities and economic stability, it is crucial to address uncertainties in coastal landslide susceptibility assessments. This necessity extends beyond scientific exploration and requires pioneering fuzzy logic methodologies, to enhance predictive accuracy. The Assaluyeh anticline is in the Persian Gulf's Zagros intercontinental region is of paramount importance, influencing Iran's energy infrastructure. To comprehend coastal landslide risk, our innovative study focuses on the Assaluyeh anticline's coastal areas. We combine 13 conditioning factors, blending traditional geological characteristics with advanced technology, including lithology, land-cover, topographic wetness index (TWI), elevation, and slope angle, using AI-driven modeling to create predictive landslide susceptibility maps. This approach surpasses conventional techniques, offering improved early warning systems and disaster management. Our research's groundbreaking aspect is its forward-looking approach, utilizing fuzzy logic to adapt to real-world conditions by embracing imprecision and uncertainty in data. We integrate historical development data, local knowledge, and forward-thinking fuzzy logic techniques to promote community-driven risk management and resilience in coastal environments. This study envisions the future, revolutionizing coastal landslide assessment and management, inspiring interdisciplinary collaboration, community engagement, and fuzzy logic-driven innovation in coastal disaster risk reduction on a global scale. [ABSTRACT FROM AUTHOR]