Correction to: Using hybrid artificial intelligence approach based on a neuro‑fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide mapping framework that involves adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods grey wolf optimization (GWO), particle swarm (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan Iran. To achieve this goal, 10 occurrence-related influencing factors were pondered. A sum 766 locations with potentiality was recognized context study, Pearson correlation technique utilized order to select models. association between landslides conditioning also evaluated using probability certainty factor (PCF) model. In next phase, data mining techniques united ANFIS model, comprising ANFIS-grey (ANFIS-GW), ANFIS-particle (ANFIS-PSO), ANFIS-shuffled (ANFIS-SFLA), structured by training dataset. Lastly, receiver operating characteristic (ROC) statistical procedures aim validating contrasting predictive capability findings study terms method importance ranking area uncovered slope, aspect, normalized difference vegetation index (NDVI), elevation have highest impact occurrence landslide. All all, ANFIS-PSO model had high performance both (RMSE = 0.288, MAE = 0.069, AUC = 0.89) validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, ANFIS-GWO ANFIS-SFLA demonstrated second third rates. Besides, revealed benefiting proper selection could facilitate modeling.