Using hybrid artificial intelligence for landslide modeling
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
Solmaz Abdollahizad
1
,
Kayvan Asghari
2
,
majid samadzamin
3
1 - 1) Department of Computer Engineering, Sardroud Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Department of Computer Engineering, Sardroud Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Department of Computer Engineering, Sardroud Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran
الکلمات المفتاحية: Machine learning, Landslide, ANFIS,
ملخص المقالة :
The current analysis exploited a hybrid ANFIS model that was optimized using PSO, GWO, and SFLA, three evolutionary algorithms. Three common models, MLP, RF, and SVM, were used to test and evaluate their performance on the same training and validation datasets, to build a LSM in EAP, Iran. For analyzing the associations between landslides and landslide conditioning variables, the PCF model was exploited as a bivariate statistical test. Furthermore, in the present analysis, the Pearson correlation test was used to measure the predictive strength of ten landslide condition variables. The fuzzy c-means clustering approach was then used to construct an initial fuzzy inference system for LSM. In addition, three wise algorithms, namely GWO, SFLA, and PSO, were used to train the ANFIS in the current analysis. One of the most significant benefits of these approaches is that they improve precision by optimizing and calculating ANFIS parameters. Indeed, it has the potential to reduce dimension dangers and the problems of local minimum, thus improving the ANFIS model accuracy. Lastly, ROC curves were used to test the LSMs generated by ANFIS-GWO, ANFIS-SFLA, and ANFIS-PSO. According to the results, the AUC values for the ANFIS-PSO, ANGIS-SFLA, and ANFIS-GWO models were 0.89, 0.88, and 0.88, respectively.
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