Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey

Nefeslioğlu, Hakan A.; Sezer, Ebru Akçapınar; Gökçeoğlu, Candan; Bozkır, Ahmet Selman; Duman, Tamer Y.

doi:10.1155/2010/901095

Cited by 218 publications

(66 citation statements)

References 43 publications

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“…The models include frequency ratio [14,15], weight of evidence [16], logistic regression [17], and fuzzy logic [18]. Recently, data mining techniques have been developed and are extremely popular [19,20] when dealing with a variety of nonlinear issues. Techniques applied in landslide susceptibility modeling include: artificial neural network, decision tree, boosted tree, neuro fuzzy, Bayesian network, support vector machine, and random forest [21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Lee

2017

Applied Sciences

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Abstract:The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN) and boosted tree (BT), for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

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Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Lee

2017

Applied Sciences

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“…These segments construct an inverted decision tree that originates with a root node at the top of the tree. The object of analysis is reflected in this root node as a simple, one-dimensional display in the decision tree interface (Nefeslioglu et al, 2010). The name of the field of data that is the object of analysis is usually displayed, along with the spread or distribution of the values that are contained in that field.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The values in the input field are used to estimate the likely value in the target field. The target field is also called an outcome, response, or dependent field or variable (Nefeslioglu et al, 2010;Sindhu et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Applying decision tree models to SMEs: A statistics-based model for customer relationship management

Hendalianpour¹,

Razmi²,

Sarvestani³

2016

10.5267/j.msl

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Customer Relationship Management (CRM) has been an important part of enterprise decisionmaking and management. In this regard, Decision Tree (DT) models are the most common tools for investigating CRM and providing an appropriate support for the implementation of CRM systems. Yet, this method does not yield any estimate of the degree of separation of different subgroups involved in analysis. In this research, we compute three decision-making models in SMEs, analyzing different decision tree methods (C&RT, C4.5 and ID3). The methods are then used to compute ME and VoE for the models and they were then used to calculate the Mean Errors (ME) and Variance of Errors (VoE) estimates to investigate the predictive power of these methods. These decision tree methods were used to analyze small-and medium-sized enterprises (SME's) datasets. The paper proposes a powerful technical support for better directing market tends and mining in CRM. According to the findings, C&RT shows a better degree of separation. As a result, we recommend using decision tree methods together with ME and VoE to determine CRM factors.

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“…Data mining includes multiple steps, i.e., data selection, pre-processing and transformation, analysis with computational algorithms, interpretation and evaluation of the results [14]. 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].…”

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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Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey

Cited by 218 publications

References 43 publications

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Applying decision tree models to SMEs: A statistics-based model for customer relationship management

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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