Landslide susceptibility assessment by novel hybrid machine learning algorithms
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 altern...
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| Format: | Article |
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MDPI AG
2019
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| _version_ | 1796864593681711104 |
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| author | Pham, Binh Thai Shirzadi, Ataollah Shahabi, Himan Omidvar, Ebrahim Singh, Sushant K. Sahana, Mehebub Asl, Dawood Talebpour Ahmad, Baharin Quoc, Nguyen Kim Lee, Saro |
| author_facet | Pham, Binh Thai Shirzadi, Ataollah Shahabi, Himan Omidvar, Ebrahim Singh, Sushant K. Sahana, Mehebub Asl, Dawood Talebpour Ahmad, Baharin Quoc, Nguyen Kim Lee, Saro |
| author_sort | Pham, Binh Thai |
| collection | ePrints |
| description | 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 (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 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. |
| first_indexed | 2024-03-05T20:44:14Z |
| format | Article |
| id | utm.eprints-88236 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:44:14Z |
| publishDate | 2019 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | utm.eprints-882362020-12-15T00:18:59Z http://eprints.utm.my/88236/ Landslide susceptibility assessment by novel hybrid machine learning algorithms Pham, Binh Thai Shirzadi, Ataollah Shahabi, Himan Omidvar, Ebrahim Singh, Sushant K. Sahana, Mehebub Asl, Dawood Talebpour Ahmad, Baharin Quoc, Nguyen Kim Lee, Saro QE Geology 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 (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 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. MDPI AG 2019-08 Article PeerReviewed Pham, Binh Thai and Shirzadi, Ataollah and Shahabi, Himan and Omidvar, Ebrahim and Singh, Sushant K. and Sahana, Mehebub and Asl, Dawood Talebpour and Ahmad, Baharin and Quoc, Nguyen Kim and Lee, Saro (2019) Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability (Switzerland), 11 (16). p. 4386. ISSN 2071-1050 http://dx.doi.org/10.3390/su11164386 |
| spellingShingle | QE Geology Pham, Binh Thai Shirzadi, Ataollah Shahabi, Himan Omidvar, Ebrahim Singh, Sushant K. Sahana, Mehebub Asl, Dawood Talebpour Ahmad, Baharin Quoc, Nguyen Kim Lee, Saro Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title | Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title_full | Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title_fullStr | Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title_full_unstemmed | Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title_short | Landslide susceptibility assessment by novel hybrid machine learning algorithms |
| title_sort | landslide susceptibility assessment by novel hybrid machine learning algorithms |
| topic | QE Geology |
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