Shallow Landslide Susceptibility Mapping for Zagreb Hilly Area, Croatia

2014 ◽  
pp. 527-531
Author(s):  
Chunxiang Wang ◽  
Snjezana Mihalić Arbanas ◽  
Hideaki Marui ◽  
Naoki Watanabe ◽  
Gen Furuya
Keyword(s):  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Hilly Area

scholarly journals Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 421 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Wei Chen ◽  
John J Clague ◽  
...  
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Characteristic Curve ◽  
Model Performance ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Conditioning Factors

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

scholarly journals Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Bahareh Ghasemain ◽  
Dawod Talebpoor Asl ◽  
Binh Thai Pham ◽  
Mohammadtghi Avand ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Regression Tree ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Landslide Susceptibility Mapping ◽  
Positive Rate ◽  
Classification And Regression ◽  
Cart Model

Shallow landslides through land degrading not only lead to threat the properly and life of human but they also may produce huge ecosystem damages. The aim of this study was to compare the performance of two decision tree machine learning algorithms including classification and regression tree (CART) and reduced error pruning tree (REPTree) for shallow landslide susceptibility mapping in Bijar, Kurdistan province, Iran. We first used 20 conditioning factors and then they were tested by information gain ratio (IGR) technique to select the most important ones. We then constructed a geodatabase based on the selected factors along with a total of 111 landslide locations with a ratio of 80/20 (for calibration/validation). The performance of the models was checked by the true positive rate (TP Rate), false positive rate (FP Rate), precision, recall, F1-Measure, Kappa, mean absolute error, and area under the receiver operatic curve (AUC). Results of IGR specified that the slope angle and TWI had the most contribution to shallow landslide occurrence in the study area. Moreover, results concluded that although these models had a high goodness-of-fit and prediction accuracy, the CART model (AUC=0.856) outperformed the REPTree model (AUC=0.837). Therefore, the CART model can be used as a promising tool and also as a base classifier to hybrid with optimization algorithms and Meta classifiers for spatial prediction of shallow landslide-prone areas.

Shallow landslide susceptibility mapping using high-resolution topography for areas devastated by super typhoon Haiyan

Landslides ◽  
2015 ◽  
Vol 13 (1) ◽  
pp. 201-210 ◽  
Author(s):  
Maricar L. Rabonza ◽  
Raquel P. Felix ◽  
Alfredo Mahar Francisco A. Lagmay ◽  
Rodrigo Narod C. Eco ◽  
Iris Jill G. Ortiz ◽  
...  
Keyword(s):  
High Resolution ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Super Typhoon

scholarly journals Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (23) ◽  
pp. 4776
Author(s):  
Taskin Kavzoglu ◽  
Alihan Teke ◽  
Elif Ozlem Yilmaz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Short Term Memory ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Rank Test ◽  
Susceptibility Map ◽  
Signed Rank Test

Natural disaster impact assessment is of the utmost significance for post-disaster recovery, environmental protection, and hazard mitigation plans. With their recent usage in landslide susceptibility mapping, deep learning (DL) architectures have proven their efficiency in many scientific studies. However, some restrictions, including insufficient model variance and limited generalization capabilities, have been reported in the literature. To overcome these restrictions, ensembling DL models has often been preferred as a practical solution. In this study, an ensemble DL architecture, based on shared blocks, was proposed to improve the prediction capability of individual DL models. For this purpose, three DL models, namely Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), together with their ensemble form (CNN–RNN–LSTM) were utilized to model landslide susceptibility in Trabzon province, Turkey. The proposed DL architecture produced the highest modeling performance of 0.93, followed by CNN (0.92), RNN (0.91), and LSTM (0.86). Findings proved that the proposed model excelled the performance of the DL models by up to 7% in terms of overall accuracy, which was also confirmed by the Wilcoxon signed-rank test. The area under curve analysis also showed a significant improvement (~4%) in susceptibility map accuracy by the proposed strategy.

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