A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

Asadi, Mitra; Mokhtari, Leila Goli; Shirzadi, Ataollah; Shahabi, Himan; Bahrami, Shahram

doi:10.1007/s12665-021-10152-4

Cited by 19 publications

(12 citation statements)

References 84 publications

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“…From a total of 118 landslide points detected in the study area, 94 points (~80%) were used as the training dataset, and 24 points (~20%) were considered as the validation dataset. A total of 118 landslide locations used in this study were a part of a total of 175 landslide locations of Asadi et al [41].…”

Section: Data 221 Landslide Inventory Mapmentioning

confidence: 99%

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A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

Ghasemian

Shahabi

Shirzadi

et al. 2022

Sensors

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We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

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Section: Data 221 Landslide Inventory Mapmentioning

confidence: 99%

“…The purpose of this study is to evaluate a robust deep-learning model that will support landslide susceptibility mapping. Here, we build on previous landslide susceptibility modelling for this study area using landslide data belonging to Asadi et al [41], but with a different set of algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

Ghasemian

Shahabi

Shirzadi

et al. 2022

Sensors

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“…The average annual rainfall for the period from 2001 to 2019 ranges from 438 to 560 mm and the average annual temperature is 14.15 °C. Based on the De Martonne climatic classification index, the climate of the study area is semi-arid climate (Asadi et al, 2022). Bedrock geology belongs to the structural zone of Sanandaj-Sirjan and the high Zagros zone, typified by basalts and shales with the intercalations of volcanic rocks.…”

Section: Study Areamentioning

confidence: 99%

“…The study area involved by active faults from the Zagros Main Recent Fault and its formations such as marl and shale and also due to the topographical conditions and geomorphological process (steep slopes and soil erosion) and also anthropogenic factor or improper human interferences (e.g., road construction on Kashtar to Yozidar route and the removal of slope bases) resulted in some landslides occurrence, considering the study area as one of the most susceptible regions of the Kurdistan Province and the country (Asadi et al, 2022). Landslides of the study area are typically shallow with the rupture surfaces less than 2-3 m depth.…”

Section: Study Areamentioning

confidence: 99%

“…The main contribution of our study is to explore how the RoF classifier and a random forest (RF) decision tree classifier generate a hybrid predictive model, called RoFRF, that provides an opportunity to pilot hybrid modeling of LSM and insights into feature screening and selection and parameter optimization. We developed the hybrid RoFRF model using the datasets belonging to Asadi et al (2022) and Ghasemian et al (2022) from the Kamyaran area in the Kurdistan Province, Iran, but with a different set of algorithms and results.…”

Section: Introductionmentioning

confidence: 99%

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Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Ghasemian

Shahabi

Shirzadi

et al. 2022

Front. Environ. Sci.

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Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.

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Land Use and Land Cover as a Conditioning Factor in Landslide Susceptibility: A Literature Review

Upadhyay,

Dixit

2024

Advances in Natural and Technological Hazards Research

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A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

Cited by 19 publications

References 84 publications

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran

Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Land Use and Land Cover as a Conditioning Factor in Landslide Susceptibility: A Literature Review

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