Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data help decision‐makers, for example, local authorities other organizations, mitigating landslide risk, although accuracy existing studies needs be improved. This study aims assess susceptibility Thua Thien Hue province Vietnam using deep neural networks (DNNs) swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm (TSO), Sand Cat (SCSO), Honey Badger Algorithm (HBA), Marine Predators (MPA) Particle (PSO). The locations 945 landslides occurring between 2012 2022, along with 14 conditioning factors, were used as input build DNN DNN‐hybrid models. performance proposed models was evaluated statistical indices receiver operating characteristic curve, area under curve (AUC), root mean square error, absolute error (MAE), R 2 accuracy. All had a high prediction. DNN‐MPA model highest AUC value (0.95), followed by DNN‐HBA DNN‐ARO DNN‐Adam DNN‐SGD DNN‐TSO (0.93), DNN‐PSO (0.9) finally DNN‐SCSO (0.83). High‐precision have identified that majority western region is very highly susceptible landslides. Models like aforementioned ones support decision‐makers updating large‐scale sustainable land‐use strategies.