An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan

Dou, Jie; Yamagishi, Hisakazu; Pourghasemi, Hamid Reza; Yunus, Ali P.; Song, Xuan; Xu, Yueren; Zhu, Zhongfan

doi:10.1007/s11069-015-1799-2

Cited by 195 publications

(101 citation statements)

References 44 publications

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“…At present, universally-applied geological hazard assessment models include subjective inference analysis models, statistical analysis models, deterministic models, pattern recognition models and the like. Then, different scholars use a variety of theories to study landslide susceptibility assessments, including such techniques as artificial neural networks [5][6][7], logistic regression [8][9][10][11], analytic hierarchy processes (AHP) [2,12,13], the information value method [14][15][16], the certainty factor [17][18][19], fuzzy logic [20][21][22] and an index of entropy [23][24][25]. These methods are mainly based on the analysis of the distributions of landslide hazards and the relationship between the influencing factors.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Zhao

Yao

Mei

et al. 2017

Entropy

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Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen'an County, Shan'xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert's knowledge and the opinions of decision-makers.

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Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Zhao

Yao

Mei

et al. 2017

Entropy

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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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“…They have important significance in slope stability evaluation, slope safety early warning, and slippery slope hazard control for timely grasping of the slope deformation evolution rules and accurate prediction of future evolution rules and trends of slope deformation [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie

Liang

Zhong

et al. 2017

IJGI

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Abstract:Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.

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An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan

Cited by 195 publications

References 44 publications

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

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

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

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