Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods

Bourenane, Hamid; Guettouche, Mohamed Saïd; Bouhadad, Youcef; Braham, Massinissa

doi:10.1007/s12517-015-2222-8

Cited by 72 publications

(37 citation statements)

References 42 publications

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“…Although domain-knowledge-driven qualitative approach is advantageous in predicting landslides, data-driven quantitative methods are widely used because collecting field data from landslide areas are challenging and hard to acquire [3]. Pourghasemi et al [14] reported that a variety of quantitatively-statistical, multi-criteria decision making, and machine learning-methods have been applied for predicting landslide susceptibility, of which logistical regression [15][16][17][18] is the most frequently used method, followed by the frequency ratio [19,20], weights-of-evidence [18,21], artificial neural networks [22,23], analytic hierarchy process [24,25], statistical index [26], index of entropy [27][28][29][30], and support vector machine [31,32]. Environmental data collected from fields as well as extracted from satellite images to develop landslide prediction models are diverse in nature, and therefore prone to inaccuracies [13].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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“…Over the years, both types of methods have been used in conjunction with geographic information systems (GIS) to assess landslide susceptibility (Dahal et al 2008;Regmi et al 2010;Mohammady et al 2012;Kayastha et al 2013;Bourenane et al 2015;Sujatha 2017;Sujatha and Sridhar 2017). There are many statistical models used in landslide hazard analysis, including the frequency ratio method (Mohammady et al 2012;Kayastha 2015;Bourenane et al 2016;Chen et al 2017a), statistical index method (Bui et al 2011;Pourghasemi et al 2013a;Razavizadeh et al 2017), weights of evidence method (Dahal et al 2008;Razavizadeh et al 2017), certainty factor method (Kanungo et al 2011;Sujatha et al 2012), and logistic regression method (Ayalew and Yamagishi 2005;Lee and Pradhan 2007;Yilmaz 2009;Chen et al 2017c). Other varieties of classification techniques, such as fuzzy systems (Oh and Pradhan 2011;Sezer et al 2011), decision-trees (Saito et al 2009;Pradhan 2013), neural networks (Yesilnacar and Topal 2005;Ermini et al 2005;Pham et al 2017), and support vector machines (Yao et al 2008;Pradhan 2013), have been used to assess landslide risk.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Wang

Guo

et al. 2019

Geomatics, Natural Hazards and Risk

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Landslide susceptibility mapping is essential in delineating landslide prone areas in mountainous regions. The primary purpose of this study is to evaluate landslide susceptibility mapping using three methods, information value (IV), weights-of-evidence (WofE), and certainty factor (CF) in southeastern Gansu province, China. Firstly, the landslide in the study area was located mainly using aerial-photo interpretation and field surveys. A total of 529 landslides were randomly divided using a ratio of 70/30 for training and validation of these methods. Eight landslide conditioning factors including slope angle, altitude, NDVI, distance to faults, distance to roads, distance to rivers, rainfall, and lithology, were considered. Landslide susceptibility maps were produced for each of the three methods. Susceptibility maps were verified and compared using the area under the curve (AUC) method. The success rate curve demonstrated that the AUC for the IV, WofE, and CF methods was 88.02, 87.56, and 89.04%, respectively, and the prediction rate curve showed that the AUC was 86.24, 86.34, and 87.44%, respectively. Furthermore, results showed that CF method had the highest accuracy in comparison with other methods. Generally, the three methods showed reasonable accuracy in landslide susceptibility mapping. Results of this study can serve as guidelines to managers and policy makers regarding the prevention and mitigation of landslide hazards.

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“…Hazard assessment includes various methods, such as a fire and explosion index (F&EI, [15,16]), a fuzzy risk assessment [17,18], and an analytic hierarchy process (AHP, [19,20]), among others. However, the set pair analysis (SPA) proposed by Zhao [21] can also be used in hazard assessment.…”

Section: Introductionmentioning

confidence: 99%

Application of a Cloud Model-Set Pair Analysis in Hazard Assessment for Biomass Gasification Stations

Yan

2017

PLoS ONE

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Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that, two biomass gasification stations undergo hazard assessment via CM-SPA and AHP based SPA, respectively. The comparison of assessment results illustrates that the CM-SPA is suitable and effective for the hazard assessment of a biomass gasification station and that CM-SPA will make the assessment results more reasonable and scientific.

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Landslide hazard mapping in the Constantine city, Northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods

Cited by 72 publications

References 42 publications

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Application of a Cloud Model-Set Pair Analysis in Hazard Assessment for Biomass Gasification Stations

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