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Convolutional neural network (CNN) is a widely used method in solving classification and regression applications in industries, engineering, and science. This study investigates the optimizing capability of a swarm intelligence algorithm named moth flame optimizer (MFO) for the optimal search of a CNN hyper-parameters (values of filters) and weights of fully connected layers. The proposed model was run with a 3-dimensional dataset (7 width × 7 height ×12 depth), which was constructed through including seven neighbor pixels (vertically and horizontally) from landslide location and 12 predictor variables. Muong Te district, Lai Chau province, Vietnam was selected as the case study, as it had recently undergone severe impacts of landslides and flash floods. The performance of this proposed model was compared with conventional classifiers, i.e., Random forest, Random subspace, and CNN-optimized Adaptive …