Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques

Naemitabar, Mahnaz; Asadi, Mohammadali Zanganeh

doi:10.1007/s11069-021-04805-7

Cited by 10 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The trend on case study location shown in the Figure 2d indicates China (35) as the most popular case study location. Other countries, including Turkey (13), Iran (10), India (9), and South Korea (8), were the other popular choice as case study locations.…”

Section: Current Trendmentioning

confidence: 99%

“…The study contribution includes; comprehensive reviews focusing exclusively on the use of ML in landslide susceptibility mapping to present the complexities, comparisons, challenges, and opportunities for future works. Naemitabar et al [9] have also carried out a comparative study of popular ML methods used in the generation of LSM. The study's primary focus is prioritizing effective landslide causative factor (LCF) to improve performance accuracy.…”

mentioning

confidence: 99%

“…There are few review papers or comparative studies [7][8][9][10] on ML-based landslide susceptibility mapping. Technology is constantly evolving rapidly, and better ML models are being introduced in landslide studies each passing day.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

View full text Add to dashboard Cite

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.

show abstract

Section: Current Trendmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Among many types of geological disasters, such as land subsidence and mudslides, landslides are the most common ones (Abedi Gheshlaghi and Feizizadeh, 2021). Because they are frequent, destructive, and widespread, every country attaches great importance to the monitoring and prevention of landslides (Naemitabar and Zanganeh Asadi, 2021). About twothirds of China's area is a mountainous region, where landslides are most prone to occur (Gautam et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Sun

Wen

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

This study aims to develop a logistic regression model of landslide susceptibility based on GeoDetector for dominant-factor screening and 10-fold cross validation for training sample optimization. First, Fengjie county, a typical mountainous area, was selected as the study area since it experienced 1,522 landslides from 2001 to 2016. Second, 22 factors were selected as the initial conditioning factors, and a geospatial database was established with a grid of 30 m precision. Factor detection of the geographic detector and the stepwise regression method included in logistic regression were used to screen out the dominant factors from the database. Then, based on the sample dataset with a 1:10 ratio of landslides and nonlandslides, 10-fold cross validation was used to select the optimized sample to train the logistic regression model of landslide susceptibility in the study area. Finally, the accuracy and efficiency of the two models before and after screening out the dominant factors were evaluated and compared. The results showed that the total accuracy of the two models was both more than 0.9, and the area under the curve value of the receiver operating characteristic curve was more than 0.8, indicating that the models before and after screening factor both had high reliability and good prediction ability. Besides, the screened factors had an active leading role in the geospatial distribution of the historical landslide, indicating that the screened dominant factors have individual rationality. Improving the geospatial agreement between landslide susceptibility and actual landslide-prone by the screening of dominant factors and the optimization of the training samples, a simple, efficient, and reliable logistic-regression–based landslide susceptibility model can be constructed.

show abstract

“…When comparing machine learning models for predicting landslide susceptibility, for instance, Zhang et al (Zhang et al, 2021) and Huang et al (Huang et al, 2017) found that SVM models offer the advantage of more consistent prediction outcomes and greater acceptability. Decision trees and RF models, according to Naemitabar et al (Naemitabar et al, 2021) and Dou et al (Dou et al, 2019), demonstrate notable advantages over other machine learning models for modelling landslide susceptibility. Even RF models demonstrate advantages over recently developed deep learning models in terms of high modelling efficiency and more reliable prediction accuracy.…”

mentioning

confidence: 99%

Research on landslide master control factor identification and susceptibility prediction modelling

Xing,

Chen,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. It is important to properly identify the primary control elements of landslide susceptibility because the modelling process and its uncertainties differ between machine learning predictions of susceptibility to landslides. In response to the aforementioned issues, the novel "weight mean method" is suggested to determine more precise landslide master factors. Support vector machine (SVM) and random forest (RF) are used as examples to discuss the prediction of landslide susceptibility and its uncertainty based on machine learning. For Ruijin City, Jiangxi Province, the landslide inventory and 12 different types of underlying environmental factors were acquired, and the factor frequency ratios were employed as input variables for SVM and RF. The landslides and randomly selected non-landslide samples were then divided into training and test sets, and the trained machine learning was used to predict and map landslide susceptibility. In order to assess modeling uncertainty and determine the landslide master control factor, subject work curves, means, and standard deviations were used. The results show that: (1) Machine learning can effectively predict regional landslide susceptibility, the accuracy of landslide susceptibility predicted by RF is higher than that of SVM, while its uncertainty is lower than that of SVM, but the overall susceptibility distribution patterns of both are similar. (2) The weight-mean approach determines that the slope, height, and lithology, in that order, are the primary controlling elements of the landslide in Ruijin City. In comparison to other machine learning models, the case studies and literature study demonstrate how dependable and susceptible the RF model is.

show abstract

Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques

Cited by 10 publications

References 23 publications

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

Improving Geospatial Agreement by Hybrid Optimization in Logistic Regression-Based Landslide Susceptibility Modelling

Research on landslide master control factor identification and susceptibility prediction modelling

Contact Info

Product

Resources

About