Mapping of Mount Semeru Volcanic Mudflow Susceptibility Along the Rejali River using the GIS-based AHP-TOPSIS Ensemble Approach - Sonia Oktariyanti; Entin Hidayah; Saifurridzal; Mokhammad Farid Ma'ruf; Nunung Nuring Hayati; Zulkifli Yusop (Journal of the Civil Engineering Forum, 2023)
Volcanic mudflow floods occur when rainfall runoff combines with volcanic material and flows downstream. These devastating events cause significant damage to infrastructure, disrupt economies, and result in injuries and casualties. One area where the flow of volcanic material greatly affects the situation is the Rejali River, which receives a substantial amount of volcanic debris from Mount Semeru. To address this issue and begin mitigating the associated risks, it is crucial to start by mapping the potential distribution of volcanic mudflow floods. Therefore, this study aimed to assess factors impacting volcanic mudflow flood susceptibility and to create a corresponding susceptibility map. The study employed the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the influence of various factors and classify the areas, respectively. These methods were integrated with the Geographic Information System (GIS) to enhance the analysis. The weighted analysis results showed that the most impactful factors conditioning volcanic mudflow floods, in descending order, were rainfall (42.40%), land cover (13.89%), elevation (13.39%), slope (12.51%), distance from the river (7.09%), soil type (6.58%), and rock distribution (4.13%). The TOPSIS calculation further highlighted that rainfall intensity between 104.03 and 109.65 mm day-1 had the greatest influence on susceptibility. The successful integration of AHP and TOPSIS methods with GIS helped develop a volcanic mudflow flood susceptibility model with an outstanding accuracy of 0.969. The model showed that approximately 46.40% of the areas along the Rejali River exhibited very high susceptibility to volcanic mudflow floods, while an additional 16.21% indicated high susceptibility and substantial risk in most regions. Therefore, the generated susceptibility map offered important insights for shaping future mitigation strategies and influencing policy decisions.