Landslides susceptibility using Fuzzy Logic approach at Kg Iboi, Baling, Kedah / Mohd Ashraf Ahmad Zubir

In numerous regions of Malaysia, the challenge of landslides poses a significant impediment to development efforts. Historical shortcomings in evaluating potential issues related to land use planning and slope management have resulted in frequent instances of damage and loss (Pradhan, 2011). The primary cause of landslides in Malaysia is heavy rainfall, whereby gravitational forces induce deformation of materials on slopes. To address these concerns, a pioneering approach involving Machine Learning and Geographic Information Systems (GIS) has been adopted to enhance image recognition capabilities. By harnessing advanced technological instruments, this endeavour aims to accurately identify and monitor slope failures. The focal point of this study is Baling, Kedah, where the ultimate objective is to proactively avert potential landslide tragedies. The primary goals encompass delineating areas susceptible to landslides through fixed wing imaging and analysing image data through the fuzzy logic machine learning. The methodology undertaken begins with the processing of fixed wing imagery and subsequent image recognition data acquisition. This process further extends to data processing for image recognition, culminating in the evaluation of the efficacy of the fuzzy model algorithm. The undertaking of this project not only advances the realm of landslide studies but also demonstrates the utilization of cutting-edge technologies. By facilitating efficient problem-solving, these approaches contribute to expediting project timelines while simultaneously enhancing their cost-effectiveness and overall quality.