A novel hybrid quantum-PSO and credal decision tree ensemble for tropical cyclone induced flash flood susceptibility mapping with geospatial data

Flash flood is considered as one of the most destructive natural hazards worldwide, especially in tropical countries, where tropical cyclones with torrential rains are recurrent problems yearly. Therefore, an accurate prediction of susceptible areas to flash floods is crucial for developing measures to prevent, avoid, and minimize damages associated with flash floods. The aim of this research is to propose a new state-of-the-art model based on hybridizing Quantum Particle Swarm Optimization (QPSO) and the Credal Decision Tree (CDT) ensemble, namely the QPSO-CDTreeEns model, for spatial prediction of the flash flood. The concept of the proposed model is to build a forest tree of the CDT established through the Random Subspace ensemble. Therein, QPSO is integrated to optimize the three parameters, the subspace size, number of trees, and the maximum depth of trees. A district suffered from a high frequency of flash floods in the north-western mountainous area of Vietnam was selected as a case study. In this regard, a geospatial database that includes a total of 1698 flash flood and inundation polygons derived from Sentinel-1 C-band SAR images and ten input indicators were used to construct and to verify the proposed model. The result shows that the QPSO-CDT-Ens model performed well (Overall accuracy = 90.4, Kappa coefficient = 0.807) and outperformed the five machine learning algorithms in flash flood susceptibility mapping. Among the ten factors, the land-use/land-cover (LULC), the slope, the curvature, and the TWI are the most important indicators. We conclude that the proposed model is a promising tool for flash flood susceptibility mapping in the tropics and may assist decision-makers in sustainable land-use planning in the national disaster mitigation strategies.