Impact of Parameter Tuning with Genetic Algorithm, Particle Swarm Optimization, and Bat Algorithm on Accuracy of the SVM Model in Landslide Susceptibility Evaluation

• Published in: Mathematical problems in engineering 2023-01, Vol.2023 (1)
• Main Authors: Yang, Cheng, Fa-you, A., Wu, Yu-Feng, Yan, Shi-qun, Zhu, Chuan-bing, Zhang, Hua
• Format: Article
• Language: English
• Subjects: Accuracy; Annual rainfall; Classification; Data collection; Genetic algorithms; Geological hazards; Geology; Landslides; Landslides & mudslides; Lithology; Machine learning; Mathematical models; Mathematical problems; Neural networks; Optimization; Parameters; Particle swarm optimization; Petroleum pipelines; Qualitative research; Regression analysis; Research methodology; Statistical methods; Support vector machines; Tuning
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2023/1393142

As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas in construction projects. More studies on landslide susceptibility using machine learning have emerged in recent years, but there is a need to study ways to draw up high-precision evaluation maps. In this article, a 60 km oil pipeline in China’s Kunming was selected as the research area. The data of 141 landslide points in the research area were obtained through field work and data collection. Meanwhile, lithology, elevation, aspect, slope, stream power index, topographic wetness index, average annual rainfall from 2017 to 2021, distance to roads, and terrain roughness were selected as causal factors of landslide susceptibility in the research area. First, the information value method was used to quantify the impact of conditional factors on landslides. Genetic algorithm (GA), particle swarm optimization (PSO), and bat algorithm (BA) were then used for parameter tuning, and the support vector machine (SVM) was used to analyze landslide susceptibility in the research area. Finally, the receiver operating characteristic (ROC) curve was used to test the model performance after parameter tuning with GA, PSO, and BA. The results show that the area-under-the-curve (AUC) values obtained through SVM, GA-SVM, PSO-SVM, and BA-SVM are 81.1%, 86.2%, 89%, and 91.8%, respectively. SVM had the best performance after parameter tuning with the BA algorithm.