Alireza Arabameri, Amir Seyed Danesh, M Santosh, Artemi Cerda, Subodh Chandra Pal, Omid Ghorbanzadeh, Paramita Roy, and Indrajit Chowdhuri

Flow chart of the study

Methodological flow chart of the study.

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds.

Geomatics, Natural Hazards and Risk, 13, 1, 949-974, 2022-04-11.

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IARAI Authors
Omid Ghorbanzadeh
Earth Observation, Weather and Climate
Flood Modeling, Genetic Algorithm, Machine Learning, Particle Swarm Optimization, Remote Sensing, Support Vector Machines


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