Integration of Insar Technique, Google Earth Images and Extensive Field Survey for Landslide Inventory in a Part of Cameron Highlands, Pahang, Malaysia

Mohammadi, Ayub; Shahabi, Himan; Ahmad, Baharin Bin

doi:10.15666/aeer/1606_80758091

Cited by 22 publications

(28 citation statements)

References 38 publications

(52 reference statements)

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“…Appropriate conditioning factors may differ from region to region, depending on geology, soils, topography, climate, and land use [161]. Thus, protocols must be developed to test the predictive ability of the entire suite of factors that are under consideration [162,163]. In this study, we prepared a landslide inventory map comprising 111 landslides and considered 20 conditioning factors.…”

Section: Discussionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

193

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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

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Section: Discussionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu

Shirzadi

Shahabi

et al. 2020

IJERPH

193

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“…Step 2: Radar data preprocessing: We coregistered radar images using the coherence between master and slave images [102]. We selected two images from 05/10/2016 and 23/11/2017 as the master images.…”

Section: Data Preprocessing and Processingmentioning

confidence: 99%

“…We produced an interferogram by multiplying the values of pixels in the master image and the conjugate complex number of related pixels in the slave image [102,103]. To detect flood-prone areas, we applied pre-and post-flood data by the interferogram formation technique.…”

Section: Data Preprocessing and Processingmentioning

confidence: 99%

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Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

et al. 2020

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Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

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“…On account of this, the making of a landside inventory is an important step to landslide susceptibility assessment. Many scholars prepare a landslide inventory using high resolution remote sensing data or aerial photograph interpretation [27,[75][76][77]. Every year the area was affected by several active landslides during rainy season or after a rainy season [78] and therefore, the Google Earth data were used to cover rainfall affected landslides.…”

Section: Landside Inventory Mapmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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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 (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 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.

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Integration of Insar Technique, Google Earth Images and Extensive Field Survey for Landslide Inventory in a Part of Cameron Highlands, Pahang, Malaysia

Cited by 22 publications

References 38 publications

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

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