A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-08, Vol.19 (16), p.3590
• Main Authors: Bui, Dieu Tien, Moayedi, Hossein, Kalantar, Bahareh, Osouli, Abdolreza, Pradhan, Biswajeet, Nguyen, Hoang, Rashid, Ahmad Safuan A
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; artificial neural network; Civil engineering; Curvature; Data mining; Elevation; Error analysis; Geographic information systems; GIS; Harris hawks optimization; Land cover; landslide susceptibility mapping; Landslides; Landslides & mudslides; Lithology; Machine learning; Mapping; Neural networks; Optimization techniques; R&D; Rainfall; Research & development
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19163590

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROC = 0.731 and AUROC = 0.777) and predicting (AUROC = 0.720 and AUROC = 0.773) the landslide pattern.