Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

Pham, Binh Thai; Bui, Dieu Tien; Prakash, Indra; Dholakia, M. B.

doi:10.1016/j.catena.2016.09.007

Cited by 518 publications

(248 citation statements)

References 49 publications

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“…Lee 2005;Goetz et al 2011Goetz et al , 2015Youssef et al 2016;Castro Camilo et al 2017) and Machine-Learning approaches (e.g. Lee et al 2004;Ermini et al 2005;Marjanovic et al 2011;Pham et al 2017) are also used in this type of analyses. Such approaches are very sensitive to the type and quality of the factors chosen for the susceptibility analysis, and the lack of suitable expert opinion can produce unreliable results (Soeters and Van Westen 1996).…”

Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

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et al. 2018

Geomatics, Natural Hazards and Risk

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In the last years, great efforts have been made to improve the assessment of the temporal and spatial occurrence of rainfall-induced shallow landslides. Therefore, in this paper we used a physically based stability model (TRIGRS) in order to reproduce the landslide event occurred in the Monterosso catchment (Cinque Terre, Eastern Liguria, Italy) on 25 October 2011. The input parameters of the numerical model have been evaluated taking into account the land-use setting and paying specific attention to the evaluation of the spatial variation of soil thickness on terraced areas. The resulting safety factor maps have been compared with the inventory map of the landslides triggered during the event. The simulation results, which have been obtained also considering four different spatial resolutions of the digital terrain model, emphasize the influence of land use in shallow landslide occurrence and indicate the importance of a realistic spatial variation of soil thickness to enhance the reliability of the model. Finally, different triggering scenarios have been defined using hourly rainfall values statistically derived from historical data. The results indicate the proneness of the area to shallow landsliding, given that rainfall events with a relatively low return period (e.g. 25 years) can trigger numerous slope failures.

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Section: Introductionmentioning

confidence: 99%

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Schilirò

Cevasco

Esposito

et al. 2018

Geomatics, Natural Hazards and Risk

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“…Results of the present study are comparable with Rodriguez et al (2006) and which showed that the Rotation Forest ensemble performs significantly better than other models such as AdaBoost and Random Forest; however, its performance is less than the MultiBoost ensemble, and quite similar to the Bagging ensemble. In comparison to other methods, the novel classifier ensemble model uses Na€ ıve Bayes classifier which has abilities to deal with uncertainty and Rotation Forest ensemble which is more effective in dealing with small sample sizes, high-dimensional and complex data structures (Pham et al 2016d).…”

Section: Discussionmentioning

confidence: 99%

“…These methods usually use new soft computing techniques that perform better than conventional methods and techniques (Pham et al 2016d).…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS

Pham

Tien

Dholakia

et al. 2016

Geomatics, Natural Hazards and Risk

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(2017) A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS, Geomatics, Natural Hazards and Risk, 8:2, 649-671, DOI: 10.1080/19475705.2016 ABSTRACTThe objective of this study is to attempt a new soft computing approach for assessment of landslide susceptibility in the Luc Yen district, Yen Bai province (Viet Nam) using a novel classifier ensemble model of Na€ ıve Bayes and Rotation Forest. First, history of 95 landslide locations was identified byfield investigations and interpretation of aerial photos. Also, the total ten landslide causal factors were selected (slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to rivers, distance to faults, and rainfall) to evaluate the spatial relationship with landslide occurrences. Information Gain technique is carried out to quantify the predictive capability of these factors. Second, landslide susceptibility assessment was carried out utilizing the novel classifier ensemble model. Finally, the performance of landslide model was validated using receiver operating characteristic curve technique, and statistical index-based evaluations. The novel classifier ensemble model indicates high prediction capability (AUC D 0.846) and relatively high accuracy (ACC D 78.77%). The study reveals that this model performs well in comparison to the other landslide models such as AdaBoost, Bagging, MultiBoost, and Random Forest. Overall, the novel classifier ensemble model is a promising method that could be used for landslide susceptibility assessment.

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“…The most common data mining methods used in landslide modeling are artificial neural networks [11,15,16], support vector machines [17][18][19][20][21], decision trees [10,20,22], and neuro-fuzzy [23,24]. Literature review shows that new data mining algorithms are suitable for landslide modeling for large and complex areas with good results [3,[25][26][27][28][29][30], and, in general, data mining models outperform conventional methods [10,[31][32][33]. However, recent studies on landslide modeling show that the overall performance of prediction models could be enhanced with the use of ensemble frameworks [31,34,35].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

et al. 2017

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This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

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Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

Cited by 518 publications

References 49 publications

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

Shallow landslide initiation on terraced slopes: inferences from a physically based approach

A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

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