Intelligent prediction and alert model of slope instability based on SSA-SVM

Aiming at the problems of low accuracy and high difficulty in slope instability prediction using traditional statistical learning models and other methods，the predicted slope database was covered by 304 slope cases from domestic and international，including the parameters such as height，angle，bulk density，cohesion，internal friction angle，pore pressure ratio and slope status，and support vector machine (SVM) was optimized by sparrow search algorithm (SSA) to construct the SSA-SVM slope instability intelligent prediction model，which could intelligently predict slope instability．To compare with the SSA-SVM model，SVM was optimized respectively by gray wolf optimization algorithm，genetic algorithm，cuckoo search algorithm，particle swarm optimization algorithm，Harris hawk optimization algorithm and whale optimization algorithm． Results show that the SSA-SVM model has outstanding advantages in slope instability prediction，and its accuracy，precision，F1-score，average precision score and area under curve (AUC) reach at 90.16%，94.28%，91.43%，96.79% and 0.954，respectively，which are higher than the corresponding indicators of other optimization models．Compared with other optimization algorithms，the SSA algorithm has strong competitiveness. © 2022 Huazhong University of Science and Technology. All rights reserved.