Landslide susceptibility modelling using integrated evidential belief function based logistic regression method: A study from Southern Western Ghats, India

Feby, Beegam; Achu, A.L.; Jimnisha, K.; Ayisha, V.A.; Reghunath, Rajesh

doi:10.1016/j.rsase.2020.100411

Cited by 36 publications

(27 citation statements)

References 37 publications

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“…Other evaluation metrics used in the present study to select the best model were ACC, SPF, SST, PPV, NPV, Kappa index, and RMSE that have widely been used in the literature [46][47][48][49][50]. SST is the ratio of landslide pixels that are correctly classified as landslide events.…”

Section: Statistical Indicesmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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Landslides are one of the most devastating natural hazards causing huge loss of life and damage to properties and infrastructures and adversely affecting the socioeconomy of the country. Landslides occur in hilly and mountainous areas all over the world. Single, ensemble, and hybrid machine learning (ML) models have been used in landslide studies for better landslide susceptibility mapping and risk management. In the present study, we have used three single ML models, namely, linear discriminant analysis (LDA), logistic regression (LR), and radial basis function network (RBFN), for landslide susceptibility mapping at Pithoragarh district, as these models are easy to apply and so far they have not been used for landslide study in this area. The main objective of this study is to evaluate the performance of these single models for correctly identifying landslide susceptible zones for their further application in other areas. For this, ten important landslide affecting factors, namely, slope, aspect, curvature, elevation, land cover, lithology, geomorphology, distance to rivers, distance to roads, and overburden depth based on the local geoenvironmental conditions, were considered for the modeling. Landslide inventory of past 398 landslide events was used in the development of models. The data of past landslide events (locations) was randomly divided into a 70/30 ratio for training (70%) and validation (30%) of the models. Standard statistical measures, namely, accuracy (ACC), specificity (SPF), sensitivity (SST), positive predictive value (PPV), negative predictive value (NPV), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC), were used to evaluate the performance of the models. Results indicated that the performance of all the models is very good (AUC > 0.90) and that of the LR model is the best (AUC = 0.926). Therefore, these single ML models can be used for the development of accurate landslide susceptibility maps. Our study demonstrated that the single models which are easy to use and can compete with the complex ensemble/hybrid models can be applied for landslide susceptibility mapping in landslide-prone areas.

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Section: Statistical Indicesmentioning

confidence: 99%

“…SST is the ratio of landslide pixels that are correctly classified as landslide events. SPF refers to pixels that are correctly classified and referred to as nonslip pixels [48]. ese indicators are calculated based on true positive (TP), true negative (TN), false positive (FP), and false negative (FN) using the following equations:…”

Section: Statistical Indicesmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Trinh

Dam

Al‐Ansari

et al. 2021

Advances in Civil Engineering

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“…The elevation data collected from DEM data for devikulam taluk and classified into 9 classes with a common interval ranges from 0-155m,155 -428m, 428 -732m, 732 -1036m,1036 -1330m, 1330 -1582m, 1582 -1802m, 1802 -2044m, 2044 -2590m above the sea level. These classes are classified using the natural break method in ArcGIS 10.4 software to get the value of jerks in terrain [31]. In Devikulam Taluk, there is a scattered shift in relative relief.…”

Section: Elevationmentioning

confidence: 99%

“…In Landsat, Band5 is a near-infrared (NIR) band, [31] and Band4 is a red band . Usually, the NDVI has five classes ranges between 0 to 1, where the minimum range represents barren land, and the maximum range represents the vegetated areas.…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

“…The LR analysis's overall objective is to define the most suitable fitting model to explain the correlation between the landslides and their parameters like slope, land cover, lithology, etc. This LR model's success is that the landslide variable can have only two options, whether an event occurred or not [47] [48] [31]. The predicted values can interpret as probability, considering all forced to happen in the interval 0 and 1 [49].…”

Section: Logistic Regressionmentioning

confidence: 99%

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Landslide Vulnerability Assessment in Devikulam Taluk, Idukki District, Kerala Using Gis and Machine Learning Algorithms

Gururajan¹,

Jawaharlal²

2021

Preprint

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Landslide is a chronic problem that causes severe geographical hazard due to development activities and exploitation of the hilly region and it occurs due to heavy and prolongs rain flow in the mountainous area. Initially, a total of 726 locations were identified at devikulam taluk, Idukki district (India). These landslide potential points utilised to construct a spatial database. Then, the geo spatial database is then split randomly into 70% for training the models and 30% for the model validation. This work considers Seven landslide triggering factors for landslide susceptibility mapping. The susceptibility maps were verified using various evaluation metrics. The metrics are sensitivity, specificity, accuracy, precision, Recall, Matthews correlation efficient (MCE), Area Under the Curve (AUC), Kappa statistics, Mean Absolute Error (MAE), Mean Square Error (MSE).The proposed work with 5 advanced machine learning approaches assess the landslide vulnerability.It includes Logistic Regression (LR), K Nearest Neighbor (KNN), Decision tree classifier, Linear Discriminant Analysis (LDA) and Gaussian Naïve Bayes modelling and comparing their performance for the spatial forecast of landslide possibilities in the Devikulam taluk. In experimental results, Decision tree classifier performs the most reliable performance with an overall accuracy rate of 99.21%.

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Landslide Hazard Risk and Vulnerability Monitoring—GIS Based Approach

Upadhyay

2024

Advances in Natural and Technological Hazards Research

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Landslide susceptibility modelling using integrated evidential belief function based logistic regression method: A study from Southern Western Ghats, India

Cited by 36 publications

References 37 publications

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

Landslide Vulnerability Assessment in Devikulam Taluk, Idukki District, Kerala Using Gis and Machine Learning Algorithms

Landslide Hazard Risk and Vulnerability Monitoring—GIS Based Approach

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