Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

An, Kyungjin; Kim, Su Yeon; Chae, Tae-Byeong; Park, Daeryong

doi:10.3390/su10020293

Cited by 17 publications

(8 citation statements)

References 45 publications

(67 reference statements)

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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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Section: Landside Inventory Mapmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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“…QGIS function is versatile as its capabilities can be extended with plugin software packages or written Python or C++ script for performing some specific functions. QGIS have been employed for numerous landslide studies, for example in [ 47 , 48 , 49 ]. Other GIS software are ArcGIS [ 50 ] and SuperMap.…”

Section: Methodsmentioning

confidence: 99%

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Tengtrairat

Woo

Parathai

et al. 2021

Sensors

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Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

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“…The ARIMA model [50] and SVR model [51][52][53][54] are more suitable for short-term prediction. In addition, other scholars applied GIS technology to evaluate landslide susceptibility [55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Bao

Zhang

et al. 2021

Energies

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The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

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Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources

Cited by 17 publications

References 45 publications

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

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