Designing gully erosion susceptibility maps (GESM) in the Algerian Eastern Tell: a case study of the K’sob River watershed

Drid, Ilhem; Achour, Yacine; Zighmi, Karim; Nouioua, Thamer; Khababa, Oussama; Rahmani, Samra; Ouadfel, Oumelkheir; Atoui, Bachir

doi:10.1007/s12517-022-10566-9

Cited by 10 publications

(3 citation statements)

References 76 publications

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“…Machine learning methods can automatically identify the hidden complex relationships between valid variables. Te results of this study are more applicable to the integrated learning algorithm for landslide susceptibility prediction when compared with the results of recent studies [27][28][29] and that the accuracy of the model is improved after the optimization of the heuristic algorithm. Te results of the integrated algorithm model difer from other studies in diferent country regions of the world, widely showing excellent landslide susceptibility predictions (AUC > 0.8).…”

Section: Comparison Of Statistical-based Models With Multiplesupporting

confidence: 53%

“…Te RF and Xgboost models had the same prediction accuracy (AUC) and better prediction performance. Drid et al [29] selected and evaluated eleven gully erosion condition factors to identify the areas most vulnerable to this hazard, and the results showed that the Xgboost model had the best predictive performance. Xgboost and GBDT have been widely used in various scenarios and achieved good results, but the single integrated learning model is afected by its parameters.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach

Zheng,

Wang,

Feng

et al. 2023

International Journal of Intelligent Systems

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Landslide susceptibility prediction is critical in open pit mines and geotechnical fields. Prediction accuracy is very essential to reduce the risk of slope instability. Traditional statistical learning methods have been widely used in early warning systems, but they cannot thoroughly explore the coupling effect among related factors, which often results in low prediction accuracy. This paper establishes an ensemble learning prediction model optimized by a genetic algorithm (GA) to determine landslide susceptibility more quickly and efficiently. The model is based on 290 sets of slope cases containing height (H), slope angle (α), unit weight (γ), cohesion (c), friction angle (φ), and pore water pressure (ru). Two common algorithms are incorporated into the ensemble learning model: Xgboost and gradient boosting decision tree (GBDT). The area under the curve (AUC) of GA-GBDT and GA-Xgboost were found to be 0.928 and 0.933, respectively, both of which could predict landslide susceptibility better. Compared with multiple logistic regression and other machine learning algorithms, both GA-GBDT and GA-Xgboost models perform better in terms of accuracy and applicability. The study results demonstrate that the developed optimized machine learning model can accurately predict landslide susceptibility and that the parameters should be optimized on a case-by-case basis to achieve more accurate results after building a suitable machine model. The optimization model proposed in this paper can be an effective new method for the intelligent prediction of landslide susceptibility.

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Section: Comparison Of Statistical-based Models With Multiplesupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach

Zheng,

Wang,

Feng

et al. 2023

International Journal of Intelligent Systems

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show abstract

“…Benefiting from the rapid development of earth observation technology and computer performance, the data‐driven approaches, represented by machine learning, have become the dominant approaches for LSE (Akgun & Erkan, 2016; Huang et al, 2020; Wu et al, 2021; Zhu et al, 2020). The frequently used machine learning models in the LSE field are support vector machines (SVM) (Chen et al, 2016; Hong et al, 2015; Pourghasemi et al, 2013; Tien Bui et al, 2016), random forests (RF) (Achour et al, 2021; Chen et al, 2018; Drid et al, 2022; Pourghasemi & Kerle, 2016), and artificial neural networks (ANN) (Chen et al, 2017; de Oliveira et al, 2019; Polykretis et al, 2015). These methods do not require specific expert knowledge and have little manual intervention, are end‐to‐end, and easy to use.…”

Section: Introductionmentioning

confidence: 99%

Geo‐environment‐aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

Zhang

Zeng

Ding

et al. 2023

Transactions in GIS

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Landslide susceptibility evaluation (LSE) is a critical issue for disaster prevention. Limited by labor cost and observation technology, landslide samples are extremely limited in dense vegetation‐covered and remote areas, making the common supervised learning model underfit with limited samples. Therefore, the reliability of analysis results in mountainous areas is low. Transfer learning can achieve reliable assessment without the need for representative samples. However, transfer learning suffers from environmental heterogeneity in regional LSE and may transfer incorrect classification knowledge of landslide features from dissimilar environments. Aiming at these challenges, we proposed a geo‐environment‐aware LSE method based on unsupervised adversarial transfer learning. The key is to consider the difference in landslide features in different geo‐environments. The study areas were first divided into multiple sub‐environments, and the similarity between the sub‐environments was calculated. Then an environment‐aware adversarial transfer model was built for fine‐grained aligning of the landslide feature with similar sub‐environments and for reducing negative transfer between dissimilar environments. The fitted classification model was employed to predict the target regions and to generate the final LSE. The experimental results indicated that the proposed method achieves reliable LSE for sample‐free regions. The accuracy of the proposed method is 7–12% better than commonly used methods such as support vector machines, random forests, and artificial neural networks. The performance of the proposed method is even close to the results of supervised learning with the presence of representative samples, and it also performs more globally and objectively in susceptibility mapping. These results reveal that the proposed method effectively transfers the knowledge of landslide susceptibility from other regions to the sample‐free region.

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RETRACTED: Systematic review on gully erosion measurement, modelling and management: Mitigation alternatives and policy recommendations

Chakrabortty

Pal

2023

Geological Journal

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The problem of “gully erosion” is one of the severe “natural hazards” associated with large‐scale land degradation in various parts of the world. Therefore, accurate modelling is one of the crucial responsibilities in this study on gully erosion. But there are still a lot of gaps in our knowledge of gully erosion and its consequences. Particularly at the regional to continental scale, this is true. Gullying is hence often forgotten about in regulation and watershed management plans. The scientific community is becoming increasingly interested in gully erosion, yet many information gaps still need to be filled. In this research, we are trying to fill the mentioned gap with the help of the existing literature. Most gully erosion research has been done to estimate the susceptible zones and ultimately select the best model. In this research, a test application has been made by considering some important models. Then the “vulnerability assessment” was done with the help of “gully erosion, climate‐induced chemical weathering and socio‐economic variables” at the village level. The village‐level information has been used to determine the vulnerability in this region. This information is beneficial for future researchers to conduct gully erosion research in various parts of the world.

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Designing gully erosion susceptibility maps (GESM) in the Algerian Eastern Tell: a case study of the K’sob River watershed

Cited by 10 publications

References 76 publications

Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach

Improving the Landslide Susceptibility Prediction Accuracy by Using Genetic Algorithm Optimized Machine Learning Approach

Geo‐environment‐aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

RETRACTED: Systematic review on gully erosion measurement, modelling and management: Mitigation alternatives and policy recommendations

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