Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models

Fallah-Zazuli, Mohammad; Vafaeinejad, Alireza; Alesheykh, Ali Asghar; Modiri, Mahdi; Aghamohammadi, Hossein

doi:10.1007/s12145-019-00389-w

Cited by 36 publications

(6 citation statements)

References 54 publications

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“…The highest AM scores represent the most relevant LCFs and vice versa. However, a factor is regarded as irrelevant, if the AM values are ≤0 (Fallah‐Zazuli et al, 2019). The gain ratio (GR) for a given LCF is computed as follows:

GR (A) = \frac{IG (A)}{E (A)},

here IG ( A ) and E ( A ) are the information gain and entropy values with respect to an attribute ‘A’.…”

Section: Methodsmentioning

confidence: 99%

“…In landslide related studies, the contribution of each LCF is derived by finding the average merit (AM) values against corresponding factors. The highest AM scores represent the most relevant LCFs and vice versa, however a factor is regarded as irrelevant, if the AM values are ≤ 0(Fallah-Zazuli et al, 2019). The gain ratio (GR) for a given LCF is computed as: (A) and E(A) are the information gain and entropy values with respect to an attribute"A".…”

mentioning

confidence: 99%

“…The highest AM scores represent the most relevant LCFs and vice versa. However, a factor is regarded as irrelevant, if the AM values are ≤0(Fallah-Zazuli et al, 2019). The gain ratio (GR) for a given LCF is computed as follows:…”

mentioning

confidence: 99%

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Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Gardezi¹,

Usmani

Chen

et al. 2022

Geological Journal

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GR (A) = \frac{IG (A)}{E (A)},

here IG ( A ) and E ( A ) are the information gain and entropy values with respect to an attribute ‘A’.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Gardezi¹,

Usmani

Chen

et al. 2022

Geological Journal

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“…As a model capable of both classification and regression, random forest overcomes problems associated with complicated modeling processes and the weak interpretative ability of other machine learning algorithms [ 23 ], and it can avoid the high sensitivity problems of neural network algorithms and have a more stable model performance [ 24 ]. This algorithm has yielded good results in geohazard susceptibility studies in geomorphologically complex areas such as the Zagros Mountain in Iran [ 25 ], Woomyeon Mountain in Korea [ 26 ], East Sikkim in India [ 27 ], and the earthquake-prone regions of Hokkaido in Japan [ 28 ], Sicily in Italy [ 29 ], and Antalya in Turkey [ 30 ]. However, random forest requires high data accuracy, and overfitting may occur when using noisy data to build a model [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Zhao

et al. 2022

IJERPH

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Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009–2012, 2013, and 2014–2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods.

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“…While (Oh and Lee 2017) utilized artificial neural networks and boosted trees to produce landslide susceptibility maps. Others like (Chen W et al 2019;Dou et al 2019;Fallah-Zazuli et al 2019;Chen 2019;Hong et al 2016;Lay et al 2019;Lee S et al 2017;Nhu et al 2020a;Song et al 2012;Tien Bui et al 2016;Vafakhah et al 2020;Vakhshoori et al 2019;Liu Z et al 2021;Oliva-Gonz alez et al 2019;Wang et al 2021) have used different machine learning algorithms to mine data and produce susceptibility maps.…”

Section: Introductionmentioning

confidence: 99%

Advanced data mining techniques for landslide susceptibility mapping

Ibrahim

Mustaffa

Balogun

et al. 2021

Geomatics, Natural Hazards and Risk

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This paper describes the development and validation of landslides susceptibility models for mountainous regions using advanced data mining techniques. The investigation was carried out to ascertain the effectiveness of Naïve Bayes Multinomial (NBM) and Random Trees (RT) in landslide susceptibility mapping. The NBM is an advancement of the frequently used Naïve Bayes classifiers, while the RT was built to overcome the limitations of the traditional forest classifiers. A geospatial database for this investigation comprises 148 landslide locations influenced by ten ( 10) landslide conditioning factors. The factors (Slope Angle, Slopes Elevation, Slope Aspect, Plan curvature, Profile Curvature, Lithology, Soil type, Stream power index (SPI), Sediment transport index (STI), and Rainfall precipitation) were drawn using a Multi Collinearity Decision Making (MCDM) technique. A Frequency Ratio (FR) analysis was used to obtain the relative significance of the factors in the slides. Predictive models were also developed by quantifying these models using data mining techniques. A section of the entire geospatial data (70%) was used as training datasets, while the remaining part of the data (30%) was used to validate the trained datasets. SVM, RT, and NBM algorithms were used to produce predicted datasets from the training datasets. These predicted datasets were used to develop the Landslides Susceptibility Models. A comparative assessment between the two classifiers against the famous traditional learning algorithm, the Support vector machines (SVM), was conducted. Model performance evaluators such as the AUROC, RSME, F-measure, MAE, and ACC were employed to check the predictive capabilities and accuracies of the models. The indices indicated that the SVM model performed better than the other two algorithms in both training and validation datasets. Further analysis and comparison of the models reveal that the new data mining techniques are reliable for landslide susceptibility. Simultaneously, the traditional algorithm is also useful and remains relevant, especially with similar site conditions. This study has provided insights on better ARTICLE HISTORY

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Mapping landslide susceptibility in the Zagros Mountains, Iran: a comparative study of different data mining models

Cited by 36 publications

References 54 publications

Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Application of data‐driven techniques for landslide susceptibility prediction along an earthquake‐affected road section in Kashmir Himalaya

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Advanced data mining techniques for landslide susceptibility mapping

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