The knowledge rules of debris flow event: A case study for investigation Chen Yu Lan River, Taiwan

Wan, Shiuan; Lei, Tsu Chiang; Huang, Peng; Chou, Tien-Yin

doi:10.1016/j.enggeo.2008.01.009

Cited by 42 publications

(20 citation statements)

References 26 publications

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“…In recent years, data mining approaches (Wan et al 2008;Lei et al 2007) offer a fresh approach to analyze landslides and geosciences. Our research used the Discrete Rough Set (DRS) method to tackle the difficult problem of landslide hazard prediction.…”

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confidence: 99%

A novel data mining technique of analysis and classification for landslide problems

2009

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Landslides during earthquakes have led to severe casualties and have resulted in damaged structures and facilities. The goal of the present study is to analyze the landslide problems in a remote area-Shei-Pa National Park in Taiwan. Spatial information techniques (Remote Sensing and Geographic Information System) with an innovative data mining technique, Discrete Rough Set (DRS) method, are incorporated to our study for analyzing landslides, their distribution, and classification. The present study provides how to find (1) the most representative data of landslide samples from the existing database, (2) the core attributes of the target categories: Normalized Difference Vegetation Index (NDVI) and Vegetation Index (VI), and (3) the thresholds (segment points) of each attribute on the target categories. A conventional approach, C4.5 Decision Tree Analysis, is used as a comparison. The methodology discussed in this study is of help to the analysis of landslide problems and thus facilitates the informed decision-making process.

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confidence: 99%

A novel data mining technique of analysis and classification for landslide problems

2009

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“…Good overview of these methods with their disadvantages and advantages can be seen in Guzzetti et al (1999), Dai et al (2002), Chacon et al (2006), and Corominas and Moya (2008). In recent years, new methods such as fuzzy logic (Lee 2007;Wan et al 2008;Kanungo et al 2009;Tangestani 2009;Biswajeet & Saied 2010;Srivastava et al 2010;Pradhan 2011aPradhan , 2011bBlais-Stevens et al 2012;Kayastha 2012;Pourghasemi et al 2012a;Tien Bui et al 2012e;Tien Bui, Ho, et al 2013;Zare et al 2013), neurofuzzy (Pradhan et al 2010b;Vahidnia et al 2010;Oh & Pradhan 2011;Sezer et al 2011;Tien Bui et al 2011), and data mining approaches methods (Melchiorre et al 2008;Nefeslioglu et al 2008;Yao et al 2008;Saito et al 2009;Yilmaz 2009;Pradhan & Lee 2010;Yeon et al 2010;Marjanovic et al 2011;Melchiorre et al 2011;Yilmaz & Kaynar 2011;Ballabio & Sterlacchini 2012;Pradhan 2013;Tien Bui et al 2012a, 2012cWan et al 2012;Xu et al 2012) have been widely used in landslide mapping. In general, the prediction capability of the new models possesses better than those obtained from...…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

Bui

Pradhan

Revhaug

et al. 2013

Geomatics, Natural Hazards and Risk

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(2015) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam), Geomatics, Natural Hazards and Risk, 6:3, 243-271, DOI: 10.1080/19475705.2013 The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas.

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“…Data mining Wan et al 2008Wan et al , 2009) had become a brand new approach in analyzing landslides and geosciences. This research used the discrete rough sets (DRS; Nguyen and Skowron 1995;Nguyen and Nguyen 1998a, b) to tackle the uncertainties arising from the materials and parameters involved in an observed landslide.…”

Section: Introductionmentioning

confidence: 99%

The knowledge expression on debris flow potential analysis through PCA + LDA and rough sets theory: a case study of Chen-Yu-Lan watershed, Nantou, Taiwan

et al. 2010

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Debris flow is often performed through identifying and analyzing the soil condition, hydraulic, geomorphological factors and vegetation conditions. In the present study, a spatial information analysis system is combined with a linear statistical method (principle components analysis with linear discriminant analysis, PCA ? LDA) and an advanced data mining technique (discrete rough sets, DRS) to investigate the debris flow occurrence based on geomorphological and vegetation conditions factors. The analyzed data sources include (1) digital elevation model: to investigate the variation in the landscape, and (2) remote sensing data: to analyze the vegetation and plant conditions on the ground surface. The objective of this research is to define a method with the ability to forecast the level of debris flow susceptibility through the parallel study of statistical outcomes (PCA ? LDA) and data mining results (DRS). The outcomes from PCA ? LDA are inadequate due to the thresholds of the influenced variables not being examined. In this study, the DRS approach not only showed satisfactory results for the thresholds of influenced variables in the study area, but also the occurrence rules of debris flow are generated. Finally, the results show superior classification accuracy (70.8% for debris flow occurrence) for the DRS method over those of PCA ? LDA analysis (54.2% for debris flow occurrence) for the analysis of debris flow occurrence. Therefore, this is an encouraging preliminary approach in the hazard assessment of debris flow.

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The knowledge rules of debris flow event: A case study for investigation Chen Yu Lan River, Taiwan

Cited by 42 publications

References 26 publications

A novel data mining technique of analysis and classification for landslide problems

A novel data mining technique of analysis and classification for landslide problems

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

The knowledge expression on debris flow potential analysis through PCA + LDA and rough sets theory: a case study of Chen-Yu-Lan watershed, Nantou, Taiwan

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