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Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models
https://orcid.org/0000-0002-7520-2104
Abstract In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models.
Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models
https://orcid.org/0000-0002-7520-2104
Abstract In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models.
Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models
https://orcid.org/0000-0002-7520-2104
Abstract In this research, landslide susceptibility map of the Fahliyan sub-basin was provided employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony optimization (ACOR) and differential evolution (DE) algorithms. Forty-three out of 61 landslides (70%) were employed to provide landslide susceptibility map and 18 landslides (30%) to validate the models. Thirteen landslide controlling factors including altitude, plan curvature, slope angle, aspect, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, TWI, SPI, land use, and lithology were employed to provide the map of landslide susceptibility. Weights of every effective factor class and effective factors were calculated based on frequency ratio of landslides relative to the class area and entropy model. The landslide susceptibility maps were generated by the GIS-based algorithms, and the resultant was validated using the training (70%) and test (30%) data of landslide locations for success and prediction rates, respectively. According to the entropy model, distance to road, rainfall, and SPI are the most effective factors on landslide occurrence in the area. The area under the curve (AUC) of ROC for the ANFIS, ANFIS-ACOR, and ANFIS-DE algorithms ranges from 0.845 to 0.946 for success rate curves and 0.793 to 0.924 for prediction rate curves, respectively. Therefore, performances of the analyzed models of landslide susceptibility are good to excellent. The success rate curves suggest that the employed algorithms have high prediction performance, but the success rate curves indicate that the ANFIS-DE algorithm has the best estimation performance (0.946) with respect to the other models.
Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models
Razavi-Termeh, Seyed Vahid (author) / Shirani, Kourosh (author) / Pasandi, Mehrdad (author)
2021
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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