GIS-based landslide susceptibility mapping using hybrid MCDM models

Jam, Amin Salehpour; Mosaffaie, Jamal; Sarfaraz, Faramarz; Shadfar, Samad; Akhtari, Rouhangiz

doi:10.1007/s11069-021-04718-5

Cited by 30 publications

(7 citation statements)

References 93 publications

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“…11), being high in all regions. Further, it can be inferred that it is as high as that in previous studies (Hasekioğulları and Ercanoglu 2012;Pourghasemi et al 2012;Roodposhti et al 2014;Myronidis et al 2016;Pereira et al 2017;Bahrami et al 2021;Salehpour Jam et al 2021) on landslide hazard mapping. Therefore, this result is considered to have the ability to classify landslide hazards.…”

Section: Relationship Between Landslide Hazard Rank and Earthquake-in...mentioning

confidence: 55%

“…In this study, the validity of the landslide hazard maps was verified using the receiver operating characteristic (ROC) curves and area under the curve (AUC) values (Fawcett 2006), in addition to the relationship between the landslide hazard rank and the distribution of landslides or slope failures. The ROC curves and AUC values are often used to verify landslide hazard maps (e.g., Hasekioğulları and Ercanoglu 2012;Pourghasemi et al 2012;Roodposhti et al 2014;Myronidis et al 2016;Pereira et al 2017;Bahrami et al 2021;Salehpour Jam et al 2021).…”

Section: Overview Of the Landslide Hazard Mapping Methods By Using Ah...mentioning

confidence: 99%

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Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination

2022

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Landslides and slope failures are often caused by earthquakes. This study proposes a method to map earthquake-induced slope failure hazards that uses the analytic hierarchy process (AHP) and a geographic information system (GIS) for four districts where many slope failures were induced by earthquakes (the 2018 Hokkaido Eastern Iburi, 2016 Kumamoto, 2008 Iwate-Miyagi Nairiku, and 2004 Mid Niigata Prefecture earthquakes). The assessment system, which was based on the National Research Institute for Earth Science and Disaster Resilience landslide distribution maps, was analyzed using the methods of previously published. We considered the relationships between the earthquake-induced slope failure distributions and landslide hazard factors (elevation, slope angle, slope type, catchment degree, geology, and vegetation). These relationships were utilized for pairwise comparisons of the factors in the AHP analysis. The slope angle, slope type, and catchment degree exerted the highest effects on the slope failure distribution in the four districts. The four earthquake-induced slope failure distributions were highly consistent with the slope failure hazard rank. These results provide a practical method for evaluating earthquake-induced slope-failure hazards.

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Section: Relationship Between Landslide Hazard Rank and Earthquake-in...mentioning

confidence: 55%

Section: Overview Of the Landslide Hazard Mapping Methods By Using Ah...mentioning

confidence: 99%

Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination

2022

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“…In 2016, the heavy metal emissions from non-ferrous metal mining and dressing in the YREB totaled 323.87 kg, among which cadmium, plumbum, and arsenic amounted to 22.27 kg, 47.32 kg, and 253.36 kg, respectively, accounting for 6.97%, 14.61%, and 78.22% of the heavy metal emissions, respectively (Figure 4). The construction of the index system is the core of WEE quality evaluation [52]. Based on the above considerations and combined with the principles of scientific, operability, and authoritativeness of data sources, the index system for evaluating the WEE of mining cities in the YREB can be constructed from two aspects: water environment and ecology.…”

Section: Construction Of An Index Systemmentioning

confidence: 99%

“…Given the impact of industrial development in mining cities, indices of water environment dimension are selected from the discharge of industrial wastewater, industrial sulfur dioxide, industrial (tobacco) dust, industrial COD, and ammonia nitrogen. China is promoting new infrastructures, and the length of sewage pipelines is considered as a part of the The construction of the index system is the core of WEE quality evaluation [52]. Based on the above considerations and combined with the principles of scientific, operability, and authoritativeness of data sources, the index system for evaluating the WEE of mining cities in the YREB can be constructed from two aspects: water environment and ecology.…”

Section: Construction Of An Index Systemmentioning

confidence: 99%

Optimizing the Water Ecological Environment of Mining Cities in the Yangtze River Economic Belt Using the Cloud Model, CV-TOPSIS, and Coupling Coordination Degree

Wang

Lin

Cheng

et al. 2022

IJERPH

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The Yangtze River Economic Belt (YREB) is the core region for the security of mineral resources in China and is a strategic water source containing rich water resources. Coordinating the security of mineral resources and water resources in the YREB is a key problem. Establishing and optimizing the water ecological environment (WEE) is crucial for addressing this problem in mining cities, which are the main bases for the supply of mineral resources. This study applies the cloud model, CV-TOPSIS, the standard deviation ellipse, and the coupling coordination degree model to evaluate the WEE and the coordinated development state, and to optimize the WEE. The results show that: (1) the WEE of mining cities in the YREB is generally good; (2) the protection of WEE in most mining cities has achieved significant results recently, and the results in the downstream are more remarkable than those in the mid-upstream; (3) the coordinated development of WEE in regenerative mining cities is better than that of mature and declining cities; and (4) most mining cities still belong to the lagging type of water environment (heavy metal pollution has been better treated and the threat of water ecological security caused by heavy metal pollution is low). This study suggests improvements to the sewer system, promotes WEE management in the mid-upstream, and propels the transformational development of mature and declining mining cities in advance.

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“…Environmental risk assessment is one of these fields that has experienced considerable improvement in recent decades 5 . Landslide susceptibility assessment is one of these fields that plays an imperative role in harnessing this hazard in prone areas 6 , 7 . As a prerequisite of many landslide susceptibility mapping approaches, a spatial database must be provided that contains the records of historical landslides, as well as relevant landslide conditioning factors (LCFs) 8 – 10 .…”

Section: Introductionmentioning

confidence: 99%

Fast hybrid methods for modeling landslide susceptibility in Ardal County

2024

Sci Rep

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Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

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GIS-based landslide susceptibility mapping using hybrid MCDM models

Cited by 30 publications

References 93 publications

Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination

Evaluating earthquake-induced widespread slope failure hazards using an AHP-GIS combination

Optimizing the Water Ecological Environment of Mining Cities in the Yangtze River Economic Belt Using the Cloud Model, CV-TOPSIS, and Coupling Coordination Degree

Fast hybrid methods for modeling landslide susceptibility in Ardal County

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