Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Acta Geophysica

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: landslide susceptibility mapping

Sortuj według:

Ogranicz wyniki do:

GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models

Mabdeh Ali Nouh, Al Fugara A’kif, Ahmadlou Mohammad, Al Adamat Rida, Al Shabeeb Abdel Rahman

Acta Geophysica

2022

Vol. 70, no 3

1253--1267

Landslides are a geological phenomenon that is causing considerable economic and human losses annually in various regions of the world. In some cases, the complex behaviors of some such phenomena cause that single machine learning models fail in modeling them well. To overcome this issue, this paper presents two novel genetic-algorithm (GA)-based ensemble models constructed with the decision tree (DT), k-nearest neighbors (KNN), and Naive Bayes (NB) models based on the bagging and random sub-space (RS) methods for landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan. Sixteen factors, including topographic, climatic, human, and geological factors were used as possible factors that influence landslide occurrence in the study area. In addition to this, one hundred and ninety two landslide locations were employed for training and testing the models. The GA was used in this study for feature selection based on three models: DT, KNN, and NB. Model performance evaluation based on the area under the receiver operating characteristic (AUROC) curve indicated that the ensemble models outperform the standalone ones. The values of the AUROC curves in the validation phases for the five models, namely, the GA-based DT, KNN, NB, bagging-based, and RS-based ensemble model, were 0.63, 0.69, 0.63, 0.89, and 0.95, respectively. The results of this study suggest that simple models can be combined using the bagging and RS methods to produce integrated models that have higher accuracy than that of any of the individual simple models.

Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modifed information value model

Akinci Halil, Yavuz Ozalp Ayse

Acta Geophysica

2021

Vol. 69, no. 3

725--745

Landslides cause loss of lives and serious material damage almost every year around the world. In Turkey, risks related to landslides increase due to the combined efects of climate change, deforestation, uncontrolled urbanization and improper land use. Artvin is among the cities where landslides occur most frequently due to its topographical and geological char acteristics. In the present study, landslide susceptibility of the Central district of Artvin was evaluated using the frequency ratio (FR) and modified information value (MIV) models. In the study, ten parameters afecting the occurrence of landslides were considered. 70% of the landslide inventories were utilized to create susceptibility maps, and the remaining 30% were utilized for validation. The success and prediction capabilities of the models were assessed using the receiver operating characteristics curve and area under the curve. The success rates of the MIV and FR models were calculated as 88% and 84.9%, respectively, and the prediction rates were computed as 82.7% and 81.9%, correspondingly. The MIV model showed a slightly better performance compared to the FR model in terms of prediction and success rates. It was also determined that most of the village built-up areas, as well as the majority of the planned areas of the Artvin Municipality and majority of the public and private buildings within the municipal boundaries, were located in landslide susceptible zones. Therefore, the outcomes of the present study may assist local administrators, planners, and engineers in reducing the damage caused by landslides and planning the optimal land use.