Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms
Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes is still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South Korea region, we coupled the convolutional neural network (CNN) with two evolutionary optimization algorithms–grey wolf optimization (GWO) and cuckoo optimization algorithm (COA). Applying geoinformatics to debris flow factors, debris-flow susceptibility maps were generated and their validities were assessed with receiver operating characteristic (ROC) curves. The results reveal that three causative factors seem to contribute most to debris flows in the study area. The evolutionary optimization algorithms achieved higher goodness-of-fit and predictive power than the standalone CNN model. The goodness-of-fit and predictive skill measures of the CNN susceptibility map were 0.76 and 0.73. The values of CNN hybridized with GWO were 0.81 and 0.81 and hybridized with COA were 0.83 and 0.82. Slope degree, tree age, stream power index, geographical class, and soil drainage were the factors most affecting debris flow likelihood. The CNN-COA is the superior model and it predicted that 40.6% of the study area (i.e., 1844.96 km2) is highly and very highly susceptible to debris flows. The methodology can be applied for analysis of other region to improve risk management and guide development and land use planning.
Li, Yang; Chen, Wei; Rezaie, Fatemeh; Rahmati, Omid; Davoudi Moghaddam, Davoud; Tiefenbacher, John; Panahi, Mahdi; Lee, Moung Jin; Kulakowski, Dominik; Tien Bui, Dieu; and Lee, Saro, "Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms" (2022). Geography. 260.