A novel explainable PSO-XGBoost model for regional flood frequency analysis at a national scale: Exploring spatial heterogeneity in flood drivers - Kanani-Sadat, Yousef; Safari, Abdolreza; Nasseri, Mohsen; Homayouni, Saeid (Journal of Hydrology, 2024)
Keywords: Infolettre; Infolettre_Ete_2024

A novel explainable stacking ensemble model for estimating design floods: A data-driven approach for ungauged regions - Kanani-Sadat, Yousef; Safari, Abdolreza; Nasseri, Mohsen; Homayouni, Saeid (Advanced Engineering Informatics, 2025)
Keywords: Infolettre_Ete_2025

A novel hybrid GIS ‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds - Sahraei, Roya; Kanani‐Sadat, Yousef; Homayouni, Saeid; Safari, Abdolreza; Oubennaceur, Khalid; Chokmani, Karem (Journal of Flood Risk Management, 2023)
Abstract Characterizing and identification of flood‐susceptible areas can be a solution to mitigate the damages and fatality rate. This study proposes a novel hybrid MCDM framework to assess flood susceptibility in large ungauged watersheds dealing with data scarcity issues. The proposed method examines the interdependencies and causal relationships between various criteria affecting the flooding procedure using the DEcision‐MAking Trial and Evaluation Laboratory (DEMATEL). Moreover, since experts' opinions contain uncertainty, the fuzzy logic is integrated with DEMATEL to overcome this shortcoming. Then, the local weights of criteria were estimated using the Best–Worst Method (BWM) to enhance the pairwise comparisons process. Final criteria weights were obtained using Fuzzy DEMATEL and BWM results in Analytical Network Process (ANP) super‐matrix. Finally, the criteria were distributed spatially using the Complex Proportional Assessment of Alternatives (COPRAS) method based on obtained weights. The proposed method was compared with different approaches such as Fuzzy‐DEMATEL ANP, BWM, and AHP using several statistical measures. We concluded that the novel hybrid proposed method outperformed other approaches based on our results. Moreover, by overlaying classified maps with the historical flood events locations, it was concluded that 85.96% of flooded areas were classified as “high” and “very high.”
Keywords: Infolettre; Infolettre_Fevrier_2023