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지질조사와 자원연구를 선도하는 한국지질자원연구원 korea institute of geoscience and mineral resouces

논문 상세정보

논문 상세정보
학회/학술지명 CATENA 주저자 Abolfazl Jaafari
발표매체 SCI 국가명 독일
논문명 국문 Meta optimization of an adaptive neuro-fuzzy inference system with greywolf optimizer and biogeography-based optimization algorithms for spatialprediction of landslide susceptibility
영문 Meta optimization of an adaptive neuro-fuzzy inference system with greywolf optimizer and biogeography-based optimization algorithms for spatialprediction of landslide susceptibility
발표일 2019.02.08
관련과제 디지털매핑에 의한 통합 지질정보 제공기술 개발 바로가기
초록 Estimation of landslide susceptibility is still an ongoing requirement for land use management plans. Here, we
proposed two novel intelligence hybrid models that rely on an adaptive neuro-fuzzy inference system (ANFIS)
and two metaheuristic optimization algorithms, i.e., grey wolf optimizer (GWO) and biogeography-based optimization
(BBO), for obtaining a reliable estimate of landslide susceptibility. Sixteen causative factors and 391
historical landslide events from a landslide-prone area of the State of Uttarakhand, northern India, were used to
generate a geospatial database. The ANFIS model was employed to develop an initial landslide susceptibility
model that was then optimized using the GWO and BBO algorithms. This resulted in two novel models, i.e.,
ANFIS-BBO and ANFIS-GWO, that benefited from an intelligent approach to automatically and properly adjust
the best parameters of the base ANFIS model for the prediction of landslide susceptibilities. The robustness of the
models was verified through a large number of runs using different splits of training and validation datasets.
Although few differences observed between the predictive capability of the models (AUCANFIS-BBO=0.95;
RMSEANFIS-BBO=0.316 vs. ACUANFIS-GWO=0.94; RMSEANFIS-GWO=0.322), the Wilcoxon signed-rank test indicated
a significant difference between the model performances in both training and validation datasets.
Overall, our proposed models demonstrated an improved prediction of landslides compared to those achieved in
previous studies with other methods. Therefore, these novel models can be recommended for modeling landslide
susceptibility, and the modelers can easily tailor their use based on their individual circumstances.

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