Open AccessOptimization of Random Forest Model for Assessing and Predicting Geological Hazards Susceptibility in Lingyun County
Author(s) -
Chunfang Kong,
Kai Xu,
Junzuo Wang,
Yaping Tian,
Zhiting Zhang,
Zhengping Weng
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110403
Subject(s) - random forest , geologic hazards , data mining , similarity (geometry) , computer science , geology , environmental science , seismology , artificial intelligence , landslide , image (mathematics)
The random forest (RF) model is improved by the optimization of unbalanced geological hazards dataset, differentiation of continuous geological hazards evaluation factors, sample similarity calculation, and iterative method for finding optimal random characteristics by calculating out-of-bagger errors. The geological hazards susceptibility evaluation model based on optimized RF (OPRF) was established and used to assess the susceptibility for Lingyun County. Then, ROC curve and field investigation were performed to verify the efficiency for different geological hazards susceptibility assessment models. The AUC values for five models were estimated as 0.766, 0.814, 0.842, 0.846 and 0.934, respectively, which indicated that the prediction accuracy of the OPRF model can be as high as 93.4%. This result demonstrated that the geological hazards susceptibility assessment model based on OPRF has the highest prediction accuracy. Furthermore, the OPRF model could be extended to other regions with similar geological environment backgrounds for geological hazards susceptibility assessment and prediction.